
"""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


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '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       lowerCAmelCase_		(				_lowerCamelCase ):



							'''simple docstring'''





							_snake_case						      =		'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-1_2    ,				snake_case_ = False    ,				**snake_case_    ,				)       ->  Optional[int]:
													super().__init__(pad_token_id=__UpperCamelCase    ,				**__UpperCamelCase				)

													__lowerCAmelCase     =  attention_window
													__lowerCAmelCase     =  sep_token_id
													__lowerCAmelCase     =  bos_token_id
													__lowerCAmelCase     =  eos_token_id
													__lowerCAmelCase     =  vocab_size
													__lowerCAmelCase     =  hidden_size
													__lowerCAmelCase     =  num_hidden_layers
													__lowerCAmelCase     =  num_attention_heads
													__lowerCAmelCase     =  hidden_act
													__lowerCAmelCase     =  intermediate_size
													__lowerCAmelCase     =  hidden_dropout_prob
													__lowerCAmelCase     =  attention_probs_dropout_prob
													__lowerCAmelCase     =  max_position_embeddings
													__lowerCAmelCase     =  type_vocab_size
													__lowerCAmelCase     =  initializer_range
													__lowerCAmelCase     =  layer_norm_eps
													__lowerCAmelCase     =  onnx_export
class       lowerCAmelCase_		(				_lowerCamelCase ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_    ,				snake_case_ = "default"    ,				snake_case_ = None				)       ->  Any:
													super().__init__(__UpperCamelCase    ,				__UpperCamelCase    ,				__UpperCamelCase				)
													__lowerCAmelCase     =  True




							@property
							def    A__						( self				)       ->  Mapping[str, Mapping[int, str]]:
													if self.task == "multiple-choice":
																			__lowerCAmelCase     =  {0: """batch""", 1: """choice""", 2: """sequence"""}
													else:
																			__lowerCAmelCase     =  {0: """batch""", 1: """sequence"""}
													return OrderedDict(
													    [
													        ("""input_ids""", dynamic_axis),
													        ("""attention_mask""", dynamic_axis),
													        ("""global_attention_mask""", dynamic_axis),
													    ]				)




							@property
							def    A__						( self				)       ->  Mapping[str, Mapping[int, str]]:
													__lowerCAmelCase     =  super().outputs
													if self.task == "default":
																			__lowerCAmelCase     =  {0: """batch"""}
													return outputs




							@property
							def    A__						( self				)       ->  float:
													return 1e-4




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




							def    A__						( self    ,				snake_case_    ,				snake_case_ = -1    ,				snake_case_ = -1    ,				snake_case_ = False    ,				snake_case_ = None    ,				)       ->  Mapping[str, Any]:
													__lowerCAmelCase     =  super().generate_dummy_inputs(
													    preprocessor=__UpperCamelCase    ,				batch_size=__UpperCamelCase    ,				seq_length=__UpperCamelCase    ,				is_pair=__UpperCamelCase    ,				framework=__UpperCamelCase				)
													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
													__lowerCAmelCase     =  torch.zeros_like(inputs["""input_ids"""]				)
													# make every second token global
													__lowerCAmelCase     =  1

													return inputs





import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     int								=     CTRLTokenizer
				A_   :     List[str]								=     False
				A_   :     str								=     False


				def __UpperCamelCase						(							self      :  Any       )		->     Tuple:
											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											A      						=    ['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
											A      						=    dict(zip(__UpperCamelCase						, range(len(__UpperCamelCase       )       )       )       )
											A      						=    ['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
											A      						=    {'unk_token': '<unk>'}

											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['vocab_file']       )
											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['merges_file']       )
											with open(self.vocab_file						, 'w'						, encoding='utf-8'       ) as fp:
																		fp.write(json.dumps(__UpperCamelCase       ) + '\n'       )
											with open(self.merges_file						, 'w'						, encoding='utf-8'       ) as fp:
																		fp.write('\n'.join(__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  Optional[Any]						, **__UpperCamelCase      :  List[str]       )		->     List[Any]:
											kwargs.update(self.special_tokens_map       )
											return CTRLTokenizer.from_pretrained(self.tmpdirname						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  int						, __UpperCamelCase      :  Optional[int]       )		->     Optional[Any]:
											A      						=    'adapt react readapt apt'
											A      						=    'adapt react readapt apt'
											return input_text, output_text


				def __UpperCamelCase						(							self      :  List[Any]       )		->     Optional[int]:
											A      						=    CTRLTokenizer(self.vocab_file						, self.merges_file						, **self.special_tokens_map       )
											A      						=    'adapt react readapt apt'
											A      						=    'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
											A      						=    tokenizer.tokenize(__UpperCamelCase       )
											self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )

											A      						=    tokens + [tokenizer.unk_token]

											A      						=    [0, 1, 2, 4, 5, 1, 0, 3, 6]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase       )						, __UpperCamelCase       )