


'''simple docstring'''







import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A				:					Dict    						=			get_tests_dir('''fixtures/test_sentencepiece.model''')


@require_sentencepiece
class 			__lowerCamelCase			(	a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   XLMProphetNetTokenizer
    a													=   False
    a													=   True


    def        A   (  self      :							int):
           super().setUp()

           # We have a SentencePiece fixture for testing
           _A					:     str     = XLMProphetNetTokenizer(SCREAMING_SNAKE_CASE				,							keep_accents=SCREAMING_SNAKE_CASE)
           tokenizer.save_pretrained(self.tmpdirname)


    def        A   (  self      :							Tuple):
           _A					:     int     = '[PAD]'
           _A					:     Any     = 0

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE)				,							SCREAMING_SNAKE_CASE)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE)				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Any):
           _A					:     Tuple     = list(self.get_tokenizer().get_vocab().keys())

           self.assertEqual(vocab_keys[0]				,							'[PAD]')
           self.assertEqual(vocab_keys[1]				,							'[CLS]')
           self.assertEqual(vocab_keys[-1]				,							'j')
           self.assertEqual(len(SCREAMING_SNAKE_CASE)				,							1012)


    def        A   (  self      :							Union[str, Any]):
           self.assertEqual(self.get_tokenizer().vocab_size				,							1012)


    def        A   (  self      :							Optional[int]):
           _A					:     Optional[Any]     = XLMProphetNetTokenizer(SCREAMING_SNAKE_CASE				,							keep_accents=SCREAMING_SNAKE_CASE)

           _A					:     List[str]     = tokenizer.tokenize('This is a test')
           self.assertListEqual(SCREAMING_SNAKE_CASE				,							['▁This', '▁is', '▁a', '▁t', 'est'])

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE)				,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]				,							)

           _A					:     Tuple     = tokenizer.tokenize('I was born in 92000, and this is falsé.')
           self.assertListEqual(
               SCREAMING_SNAKE_CASE				,							[
                   SPIECE_UNDERLINE + 'I',
                   SPIECE_UNDERLINE + 'was',
                   SPIECE_UNDERLINE + 'b',
                   'or',
                   'n',
                   SPIECE_UNDERLINE + 'in',
                   SPIECE_UNDERLINE + '',
                   '9',
                   '2',
                   '0',
                   '0',
                   '0',
                   ',',
                   SPIECE_UNDERLINE + 'and',
                   SPIECE_UNDERLINE + 'this',
                   SPIECE_UNDERLINE + 'is',
                   SPIECE_UNDERLINE + 'f',
                   'al',
                   's',
                   'é',
                   '.',
               ]				,							)
           _A					:     Tuple     = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE)
           self.assertListEqual(
               SCREAMING_SNAKE_CASE				,							[
                   value + tokenizer.fairseq_offset
                   for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
               ]				,							)

           _A					:     Optional[Any]     = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE)
           self.assertListEqual(
               SCREAMING_SNAKE_CASE				,							[
                   SPIECE_UNDERLINE + 'I',
                   SPIECE_UNDERLINE + 'was',
                   SPIECE_UNDERLINE + 'b',
                   'or',
                   'n',
                   SPIECE_UNDERLINE + 'in',
                   SPIECE_UNDERLINE + '',
                   '[UNK]',
                   '2',
                   '0',
                   '0',
                   '0',
                   ',',
                   SPIECE_UNDERLINE + 'and',
                   SPIECE_UNDERLINE + 'this',
                   SPIECE_UNDERLINE + 'is',
                   SPIECE_UNDERLINE + 'f',
                   'al',
                   's',
                   '[UNK]',
                   '.',
               ]				,							)


    @cached_property
    def        A   (  self      :							List[Any]):
           return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')


    @slow
    def        A   (  self      :							Optional[int]):
           _A					:     List[Any]     = 'Hello World!'
           _A					:     Dict     = [35389, 6672, 49, 2]
           self.assertListEqual(SCREAMING_SNAKE_CASE				,							self.big_tokenizer.encode(SCREAMING_SNAKE_CASE))


    @slow
    def        A   (  self      :							Union[str, Any]):
           # fmt: off
           _A					:     Tuple     = {'input_ids': [[11073, 82783, 18, 26, 82783, 549, 51540, 248, 17209, 1301, 217, 20, 215186, 1325, 147, 17209, 1301, 217, 20, 56370, 53, 122020, 20, 16477, 27, 87355, 4548, 20, 4728, 78392, 17, 159969, 18, 26, 24491, 629, 15, 538, 22704, 5439, 15, 2788, 24491, 9885, 15, 43534, 605, 15, 814, 18403, 33200, 29, 15, 43534, 24458, 12410, 111, 24966, 83669, 9637, 144068, 26, 850, 22346, 27, 147, 24966, 83669, 83490, 26, 39113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 122020, 115785, 34, 816, 1339, 46887, 18, 147, 53905, 1951, 42238, 41170, 17732, 834, 436, 15, 27523, 98733, 217, 147, 5542, 4981, 930, 17347, 16, 2], [20091, 629, 94, 82786, 58, 490, 20, 1528, 84, 53905, 344, 80592, 110128, 18822, 5267, 1306, 62, 152537, 308, 7997, 401, 124427, 549, 35442, 225, 109, 15055, 25748, 147, 7119, 43712, 34, 767, 135366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 63784, 119466, 17, 147808, 88214, 18, 656, 81, 32, 3296, 10280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=SCREAMING_SNAKE_CASE				,							model_name='microsoft/xprophetnet-large-wiki100-cased'				,							revision='1acad1643ddd54a44df6a1b797ada8373685d90e'				,							)










'''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


A				:					Tuple    						=			logging.get_logger(__name__)

A				:					Tuple    						=			{
    '''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			(	a_	):
    """simple docstring"""
    a													=   "longformer"


    def __init__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Union[List[int], int] = 512				,							SCREAMING_SNAKE_CASE      :							int = 2				,							SCREAMING_SNAKE_CASE      :							int = 1				,							SCREAMING_SNAKE_CASE      :							int = 0				,							SCREAMING_SNAKE_CASE      :							int = 2				,							SCREAMING_SNAKE_CASE      :							int = 30522				,							SCREAMING_SNAKE_CASE      :							int = 768				,							SCREAMING_SNAKE_CASE      :							int = 12				,							SCREAMING_SNAKE_CASE      :							int = 12				,							SCREAMING_SNAKE_CASE      :							int = 3072				,							SCREAMING_SNAKE_CASE      :							str = "gelu"				,							SCREAMING_SNAKE_CASE      :							float = 0.1				,							SCREAMING_SNAKE_CASE      :							float = 0.1				,							SCREAMING_SNAKE_CASE      :							int = 512				,							SCREAMING_SNAKE_CASE      :							int = 2				,							SCREAMING_SNAKE_CASE      :							float = 0.02				,							SCREAMING_SNAKE_CASE      :							float = 1e-12				,							SCREAMING_SNAKE_CASE      :							bool = False				,							**SCREAMING_SNAKE_CASE      :							List[Any]				,							):
           super().__init__(pad_token_id=SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)

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




class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def __init__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							"PretrainedConfig"				,							SCREAMING_SNAKE_CASE      :							str = "default"				,							SCREAMING_SNAKE_CASE      :							"List[PatchingSpec]" = None):
           super().__init__(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
           _A					:     Optional[Any]     = True


    @property
    def        A   (  self      :							List[str]):
           if self.task == "multiple-choice":
                  _A					:     Tuple     = {0: 'batch', 1: 'choice', 2: 'sequence'}
           else:
                  _A					:     List[Any]     = {0: 'batch', 1: 'sequence'}
           return OrderedDict(
               [
                   ('input_ids', dynamic_axis),
                   ('attention_mask', dynamic_axis),
                   ('global_attention_mask', dynamic_axis),
               ])


    @property
    def        A   (  self      :							str):
           _A					:     int     = super().outputs
           if self.task == "default":
                  _A					:     str     = {0: 'batch'}
           return outputs


    @property
    def        A   (  self      :							List[Any]):
           return 1e-4


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


    def        A   (  self      :							str				,							SCREAMING_SNAKE_CASE      :							"PreTrainedTokenizerBase"				,							SCREAMING_SNAKE_CASE      :							int = -1				,							SCREAMING_SNAKE_CASE      :							int = -1				,							SCREAMING_SNAKE_CASE      :							bool = False				,							SCREAMING_SNAKE_CASE      :							Optional[TensorType] = None				,							):
           _A					:     Union[str, Any]     = super().generate_dummy_inputs(
               preprocessor=SCREAMING_SNAKE_CASE				,							batch_size=SCREAMING_SNAKE_CASE				,							seq_length=SCREAMING_SNAKE_CASE				,							is_pair=SCREAMING_SNAKE_CASE				,							framework=SCREAMING_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					:     Tuple     = torch.zeros_like(inputs['input_ids'])
           # make every second token global
           _A					:     Dict     = 1

           return inputs






