"""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 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Optional[Any]					     =				'longformer'





			def __init__(			self   ,      snake_case_ = 512   ,      snake_case_ = 2   ,      snake_case_ = 1   ,      snake_case_ = 0   ,      snake_case_ = 2   ,      snake_case_ = 30_522   ,      snake_case_ = 768   ,      snake_case_ = 12   ,      snake_case_ = 12   ,      snake_case_ = 3_072   ,      snake_case_ = "gelu"   ,      snake_case_ = 0.1   ,      snake_case_ = 0.1   ,      snake_case_ = 512   ,      snake_case_ = 2   ,      snake_case_ = 0.02   ,      snake_case_ = 1E-12   ,      snake_case_ = False   ,      **snake_case_   ,      ):







										'''simple docstring'''


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

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





class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_   ,      snake_case_ = "default"   ,      snake_case_ = None							):







										'''simple docstring'''


										super().__init__(snake_case_   ,      snake_case_   ,      snake_case_							)
										A__       :     Optional[Any]								=			True





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Union[str, Any]								=			super().outputs
										if self.task == "default":
																	A__       :     int								=			{0: """batch"""}
										return outputs





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return 1E-4





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return max(super().default_onnx_opset   ,      14							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = -1   ,      snake_case_ = -1   ,      snake_case_ = False   ,      snake_case_ = None   ,      ):







										'''simple docstring'''


										A__       :     Dict								=			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
										A__       :     int								=			torch.zeros_like(inputs["""input_ids"""]							)
										# make every second token global
										A__       :     Any								=			1

										return inputs






"""simple docstring"""



import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''





			def __init__(			self   ,      snake_case_=""   ,      snake_case_="train"							):







										'''simple docstring'''


										assert os.path.isdir(snake_case_							)

										A__       :     Dict								=			[]
										A__       :     Optional[Any]								=			os.listdir(snake_case_							)
										for story_filename in story_filenames_list:
																	if "summary" in story_filename:
																								continue
																	A__       :     Any								=			os.path.join(snake_case_   ,      snake_case_							)
																	if not os.path.isfile(snake_case_							):
																								continue
																	self.documents.append(snake_case_							)





			def __len__(			self							):







										'''simple docstring'''


										return len(self.documents							)





			def __getitem__(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     List[str]								=			self.documents[idx]
										A__       :     List[Any]								=			document_path.split("""/"""							)[-1]
										with open(snake_case_   ,      encoding="""utf-8"""							) as source:
																	A__       :     Dict								=			source.read()
																	A__				,							A__       :     Dict								=			process_story(snake_case_							)
										return document_name, story_lines, summary_lines





def  _A(		lowerCAmelCase							):
							A__       :     Optional[int]								=			list(filter(lambda lowerCAmelCase							: len(lowerCAmelCase							) != 0			,  [line.strip() for line in raw_story.split("""\n"""							)]							)							)

							# for some unknown reason some lines miss a period, add it
							A__       :     List[str]								=			[_add_missing_period(lowerCAmelCase							) for line in nonempty_lines]

							# gather article lines
							A__       :     Union[str, Any]								=			[]
							A__       :     Optional[Any]								=			deque(lowerCAmelCase							)
							while True:
														try:
																					A__       :     List[str]								=			lines.popleft()
																					if element.startswith("""@highlight"""							):
																												break
																					story_lines.append(lowerCAmelCase							)
														except IndexError:
																					# if "@highlight" is absent from the file we pop
																					# all elements until there is None, raising an exception.
																					return story_lines, []

    # gather summary lines
							A__       :     List[str]								=			list(filter(lambda lowerCAmelCase							: not t.startswith("""@highlight"""							)			,  lowerCAmelCase							)							)

							return story_lines, summary_lines

def  _A(		lowerCAmelCase							):
							A__       :     Tuple								=			[""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
							if line.startswith("""@highlight"""							):
														return line
							if line[-1] in END_TOKENS:
														return line
							return line + "."

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							if len(lowerCAmelCase							) > block_size:
														return sequence[:block_size]
							else:
														sequence.extend([pad_token_id] * (block_size - len(lowerCAmelCase							))							)
														return sequence

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Any								=			torch.ones_like(lowerCAmelCase							)
							A__       :     Any								=			sequence == pad_token_id
							A__       :     Any								=			0
							return mask

def  _A(		lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     Dict								=			[tokenizer.encode(lowerCAmelCase							) for line in story_lines]
							A__       :     Any								=			[token for sentence in story_lines_token_ids for token in sentence]
							A__       :     List[str]								=			[tokenizer.encode(lowerCAmelCase							) for line in summary_lines]
							A__       :     int								=			[token for sentence in summary_lines_token_ids for token in sentence]

							return story_token_ids, summary_token_ids



def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     str								=			[]
							for sequence in batch:
														A__       :     List[Any]								=			-1
														A__       :     Dict								=			[]
														for s in sequence:
																					if s == separator_token_id:
																												sentence_num += 1
																					embeddings.append(sentence_num % 2							)
														batch_embeddings.append(lowerCAmelCase							)
							return torch.tensor(lowerCAmelCase							)





