





import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
				import torch



class    _a     (				UpperCamelCase_							):






							"""simple docstring"""

							snake_case_							 =						"char"
							snake_case_							 =						"bpe"
							snake_case_							 =						"wp"







__lowercase       :Any       =  (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)



class    _a     (				UpperCamelCase_							):






							"""simple docstring"""

							snake_case_							 =						["image_processor", "char_tokenizer"]
							snake_case_							 =						"ViTImageProcessor"
							snake_case_							 =						"MgpstrTokenizer"

							def __init__(      self				:     Union[str, Any]							,	a				:     Any=None							,	a				:     Dict=None							,	**a				:     Any    )	->Any:
													SCREAMING_SNAKE_CASE__     :				List[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."							,	__A							,	)
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					kwargs.pop("feature_extractor"    )

													SCREAMING_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`."    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					tokenizer
													SCREAMING_SNAKE_CASE__     :				List[str]   =					AutoTokenizer.from_pretrained("gpt2"    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					AutoTokenizer.from_pretrained("bert-base-uncased"    )

													super().__init__(__A							,	__A    )

							def __call__(      self				:     List[str]							,	a				:     str=None							,	a				:     Optional[Any]=None							,	a				:     Dict=None							,	**a				:     Optional[Any]    )	->List[Any]:
													if images is None and text is None:
																			raise ValueError("You need to specify either an `images` or `text` input to process."    )

													if images is not None:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.image_processor(__A							,	return_tensors=__A							,	**__A    )
													if text is not None:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					self.char_tokenizer(__A							,	return_tensors=__A							,	**__A    )

													if text is None:
																			return inputs
													elif images is None:
																			return encodings
													else:
																			SCREAMING_SNAKE_CASE__     :				Any   =					encodings["input_ids"]
																			return inputs

							def      A_  (      self				:     Optional[Any]							,	a				:     Optional[int]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					sequences
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					char_preds.size(0    )

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self._decode_helper(__A							,	"char"    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					self._decode_helper(__A							,	"bpe"    )
													SCREAMING_SNAKE_CASE__     :				int   =					self._decode_helper(__A							,	"wp"    )

													SCREAMING_SNAKE_CASE__     :				Dict   =					[]
													SCREAMING_SNAKE_CASE__     :				Dict   =					[]
													for i in range(__A    ):
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					[char_scores[i], bpe_scores[i], wp_scores[i]]
																			SCREAMING_SNAKE_CASE__     :				str   =					[char_strs[i], bpe_strs[i], wp_strs[i]]
																			SCREAMING_SNAKE_CASE__     :				str   =					scores.index(max(__A    )    )
																			final_strs.append(strs[max_score_index]    )
																			final_scores.append(scores[max_score_index]    )

													SCREAMING_SNAKE_CASE__     :				Any   =					{}
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					final_strs
													SCREAMING_SNAKE_CASE__     :				Tuple   =					final_scores
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					char_strs
													SCREAMING_SNAKE_CASE__     :				Tuple   =					bpe_strs
													SCREAMING_SNAKE_CASE__     :				Dict   =					wp_strs
													return out

							def      A_  (      self				:     str							,	a				:     List[Any]							,	a				:     Union[str, Any]    )	->Optional[Any]:
													if format == DecodeType.CHARACTER:
																			SCREAMING_SNAKE_CASE__     :				int   =					self.char_decode
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					1
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					"[s]"
													elif format == DecodeType.BPE:
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.bpe_decode
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					2
																			SCREAMING_SNAKE_CASE__     :				Dict   =					"#"
													elif format == DecodeType.WORDPIECE:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.wp_decode
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					1_02
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					"[SEP]"
													else:
																			raise ValueError(f"""Format {format} is not supported."""    )

													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[], []
													SCREAMING_SNAKE_CASE__     :				Tuple   =					pred_logits.size(0    )
													SCREAMING_SNAKE_CASE__     :				int   =					pred_logits.size(1    )
													SCREAMING_SNAKE_CASE__     :				Any   =					pred_logits.topk(1							,	dim=-1							,	largest=__A							,	sorted=__A    )
													SCREAMING_SNAKE_CASE__     :				Tuple   =					preds_index.view(-1							,	__A    )[:, 1:]
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					decoder(__A    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					torch.nn.functional.softmax(__A							,	dim=2    ).max(dim=2    )
													SCREAMING_SNAKE_CASE__     :				str   =					preds_max_prob[:, 1:]

													for index in range(__A    ):
																			SCREAMING_SNAKE_CASE__     :				int   =					preds_str[index].find(__A    )
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					preds_str[index][:pred_eos]
																			SCREAMING_SNAKE_CASE__     :				Any   =					preds_index[index].cpu().tolist()
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					pred_index.index(__A    ) if eos_token in pred_index else -1
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					preds_max_prob[index][: pred_eos_index + 1]
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					pred_max_prob.cumprod(dim=0    )[-1] if pred_max_prob.nelement() != 0 else 0.0
																			dec_strs.append(__A    )
																			conf_scores.append(__A    )

													return dec_strs, conf_scores

							def      A_  (      self				:     Union[str, Any]							,	a				:     Tuple    )	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					[seq.replace(" "							,	""    ) for seq in self.char_tokenizer.batch_decode(__A    )]
													return decode_strs

							def      A_  (      self				:     List[Any]							,	a				:     Any    )	->Union[str, Any]:
													return self.bpe_tokenizer.batch_decode(__A    )






							def      A_  (      self				:     Optional[int]							,	a				:     List[str]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					[seq.replace(" "							,	""    ) for seq in self.wp_tokenizer.batch_decode(__A    )]
													return decode_strs













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


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

__lowercase       :Optional[int]       =  {
    "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__							):






							"""simple docstring"""

							snake_case_							 =						"longformer"

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

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







class    _a     (				lowercase__							):






							"""simple docstring"""

							def __init__(      self				:     int							,	a				:     "PretrainedConfig"							,	a				:     str = "default"							,	a				:     "List[PatchingSpec]" = None    )	->str:
													super().__init__(a							,	a							,	a    )
													SCREAMING_SNAKE_CASE__     :				Any   =					True

							@property
							def      A_  (      self				:     int    )	->Mapping[str, Mapping[int, str]]:
													if self.task == "multiple-choice":
																			SCREAMING_SNAKE_CASE__     :				int   =					{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      A_  (      self				:     Optional[Any]    )	->Mapping[str, Mapping[int, str]]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					super().outputs
													if self.task == "default":
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					{0: "batch"}
													return outputs

							@property
							def      A_  (      self				:     str    )	->float:
													return 1E-4

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






							def      A_  (      self				:     str							,	a				:     "PreTrainedTokenizerBase"							,	a				:     int = -1							,	a				:     int = -1							,	a				:     bool = False							,	a				:     Optional[TensorType] = None							,	)	->Mapping[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					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__     :				Any   =					torch.zeros_like(inputs["input_ids"]    )
													# make every second token global
													SCREAMING_SNAKE_CASE__     :				str   =					1

													return inputs