

import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


a_    :						Any    		=      get_tests_dir("fixtures/test_sentencepiece.model")




@require_sentencepiece
@require_tokenizers
class      UpperCamelCase (     __UpperCAmelCase	,			unittest.TestCase   ):
    __UpperCamelCase         =XLNetTokenizer
    __UpperCamelCase         =XLNetTokenizerFast
    __UpperCamelCase         =True
    __UpperCamelCase         =True

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     super().setUp()

     # We have a SentencePiece fixture for testing
     SCREAMING_SNAKE_CASE								=			XLNetTokenizer(snake_case__							,    keep_accents=snake_case__    )
     tokenizer.sanitize_special_tokens()
     tokenizer.save_pretrained(self.tmpdirname    )

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'<s>'
     SCREAMING_SNAKE_CASE								=			1

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

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			list(self.get_tokenizer().get_vocab().keys()    )

     self.assertEqual(vocab_keys[0]							,    '<unk>'    )
     self.assertEqual(vocab_keys[1]							,    '<s>'    )
     self.assertEqual(vocab_keys[-1]							,    '<eod>'    )
     self.assertEqual(len(snake_case__    )							,    1_0_0_6    )

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     self.assertEqual(self.get_tokenizer().vocab_size							,    1_0_0_0    )

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			XLNetTokenizer(snake_case__							,    keep_accents=snake_case__    )

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

     self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__    )							,    [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]    )

     SCREAMING_SNAKE_CASE								=			tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
     self.assertListEqual(
         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',
             '.',
         ]							,    )
     SCREAMING_SNAKE_CASE								=			tokenizer.convert_tokens_to_ids(snake_case__    )
     self.assertListEqual(snake_case__							,    [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]    )

     SCREAMING_SNAKE_CASE								=			tokenizer.convert_ids_to_tokens(snake_case__    )
     self.assertListEqual(
         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>',
             '.',
         ]							,    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			XLNetTokenizer(snake_case__							,    do_lower_case=snake_case__    )
     SCREAMING_SNAKE_CASE								=			tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
     self.assertListEqual(
         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',
             'se',
             '.',
         ]							,    )
     self.assertListEqual(tokenizer.tokenize('H\u00E9llo'    )							,    ['▁he', 'll', 'o']    )

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			XLNetTokenizer(snake_case__							,    do_lower_case=snake_case__    )
     SCREAMING_SNAKE_CASE								=			tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
     self.assertListEqual(
         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',
             'se',
             '.',
         ]							,    )

    @slow
    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			XLNetTokenizer.from_pretrained('xlnet-base-cased'    )

     SCREAMING_SNAKE_CASE								=			tokenizer.encode('sequence builders'							,    add_special_tokens=snake_case__    )
     SCREAMING_SNAKE_CASE								=			tokenizer.encode('multi-sequence build'							,    add_special_tokens=snake_case__    )

     SCREAMING_SNAKE_CASE								=			tokenizer.build_inputs_with_special_tokens(snake_case__    )
     SCREAMING_SNAKE_CASE								=			tokenizer.build_inputs_with_special_tokens(snake_case__							,    snake_case__    )

     assert encoded_sentence == text + [4, 3]
     assert encoded_pair == text + [4] + text_a + [4, 3]




    @slow
    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			{'input_ids': [[1_7, 2_1_4_4_2, 2_7_0, 1_7, 1_0, 1_4_6_4_5, 3_1_8, 3_4, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 7_7_5_2, 2_2_0_1_8, 2_3, 2_1, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 3_3_5_2, 1_4_4_3_1, 1_3, 5_5_0_0, 1_1, 1_1_7_6, 5_8_0, 1_3, 1_6_8_1_9, 4_7_9_7, 2_3, 1_7, 1_0, 1_7_1_3_5, 6_5_8, 1_9, 4_5_7, 7_9_3_2, 1_3, 1_8_4, 1_9, 3_1_5_4, 1_7_1_3_5, 6_4_6_8, 1_9, 1_4_0_4, 1_2_2_6_9, 1_9, 4_2_2_9, 5_3_5_6, 1_6_2_6_4, 4_6, 1_9, 1_7, 2_0_5_4_5, 1_0_3_9_5, 9, 9, 9, 1_1, 2_8, 6_4_2_1, 9_5_3_1, 2_0_7_2_9, 1_7, 1_0, 3_5_3, 1_7_0_2_2, 1_1, 2_1, 6_4_2_1, 9_5_3_1, 1_6_9_4_9, 1_7, 1_0, 1_1_5_0_9, 7_5_3, 1_1, 3_3, 9_5, 2_4_2_1, 7_3_8_5, 9_5_6, 1_4_4_3_1, 2_6_2_6, 2_5, 8_4_2, 7_3_8_5, 4_8_3_6, 2_1, 1_4_2_9, 2_2_7_2, 9_8_5_5, 3_1_2_0, 1_6_1, 2_4_7_3_8, 1_9, 1_3_2_0_3, 6_5_8, 2_1_8, 7_8_7, 2_1, 4_3_0, 1_8_4_8_2, 8_4_7, 2_6_3_7, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2_2, 2_2_1_7_8, 2_7, 1_0_6_4, 2_2, 9_5_6, 1_3, 1_1_1_0_1, 1_4_2_9, 5_8_5_4, 2_4_3_1_3, 1_8_9_5_3, 4_0, 4_2_2, 2_4_3_6_6, 6_8, 1_7_5_8, 3_7, 1_0_4_8_3, 1_4_2_5_7, 3_1, 2_0_7, 2_6_3, 2_1, 2_0_3, 3_7_7_3, 2_5, 7_1, 9_7_3_5, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2, 2_0_4_9, 3_4_4_2, 1_7, 1_3_8_9_4, 3_3_8_0, 2_3, 9_5, 1_8, 1_7_6_3_4, 2_2_8_8, 9, 4, 3]], 'token_type_ids': [[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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 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, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], '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], [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, 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, 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]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=snake_case__							,    model_name='xlnet-base-cased'							,    revision='c841166438c31ec7ca9a106dee7bb312b73ae511'							,    )




import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          NystromformerForMaskedLM,
          NystromformerForMultipleChoice,
          NystromformerForQuestionAnswering,
          NystromformerForSequenceClassification,
          NystromformerForTokenClassification,
          NystromformerModel,
      )
      from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST




class      UpperCamelCase :

    def __init__(			self :      List[str]							,    snake_case__ :      Dict							,    snake_case__ :      Optional[Any]=1_3							,    snake_case__ :      Union[str, Any]=7							,    snake_case__ :      List[str]=True							,    snake_case__ :      Any=True							,    snake_case__ :      List[str]=True							,    snake_case__ :      Optional[Any]=True							,    snake_case__ :      List[str]=9_9							,    snake_case__ :      str=3_2							,    snake_case__ :      Dict=5							,    snake_case__ :      str=4							,    snake_case__ :      int=3_7							,    snake_case__ :      Union[str, Any]="gelu"							,    snake_case__ :      Any=0.1							,    snake_case__ :      Dict=0.1							,    snake_case__ :      Optional[Any]=5_1_2							,    snake_case__ :      List[Any]=1_6							,    snake_case__ :      str=2							,    snake_case__ :      int=0.02							,    snake_case__ :      List[str]=3							,    snake_case__ :      Dict=4							,    snake_case__ :      str=None							,    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			parent
     SCREAMING_SNAKE_CASE								=			batch_size
     SCREAMING_SNAKE_CASE								=			seq_length
     SCREAMING_SNAKE_CASE								=			is_training
     SCREAMING_SNAKE_CASE								=			use_input_mask
     SCREAMING_SNAKE_CASE								=			use_token_type_ids
     SCREAMING_SNAKE_CASE								=			use_labels
     SCREAMING_SNAKE_CASE								=			vocab_size
     SCREAMING_SNAKE_CASE								=			hidden_size
     SCREAMING_SNAKE_CASE								=			num_hidden_layers
     SCREAMING_SNAKE_CASE								=			num_attention_heads
     SCREAMING_SNAKE_CASE								=			intermediate_size
     SCREAMING_SNAKE_CASE								=			hidden_act
     SCREAMING_SNAKE_CASE								=			hidden_dropout_prob
     SCREAMING_SNAKE_CASE								=			attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE								=			max_position_embeddings
     SCREAMING_SNAKE_CASE								=			type_vocab_size
     SCREAMING_SNAKE_CASE								=			type_sequence_label_size
     SCREAMING_SNAKE_CASE								=			initializer_range
     SCREAMING_SNAKE_CASE								=			num_labels
     SCREAMING_SNAKE_CASE								=			num_choices
     SCREAMING_SNAKE_CASE								=			scope

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size    )

     SCREAMING_SNAKE_CASE								=			None
     if self.use_input_mask:
      SCREAMING_SNAKE_CASE								=			random_attention_mask([self.batch_size, self.seq_length]    )

     SCREAMING_SNAKE_CASE								=			None
     if self.use_token_type_ids:
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.type_vocab_size    )

     SCREAMING_SNAKE_CASE								=			None
     SCREAMING_SNAKE_CASE								=			None
     SCREAMING_SNAKE_CASE								=			None
     if self.use_labels:
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size]							,    self.type_sequence_label_size    )
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.num_labels    )
      SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size]							,    self.num_choices    )

     SCREAMING_SNAKE_CASE								=			self.get_config()

     return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     return NystromformerConfig(
         vocab_size=self.vocab_size							,    hidden_size=self.hidden_size							,    num_hidden_layers=self.num_hidden_layers							,    num_attention_heads=self.num_attention_heads							,    intermediate_size=self.intermediate_size							,    hidden_act=self.hidden_act							,    hidden_dropout_prob=self.hidden_dropout_prob							,    attention_probs_dropout_prob=self.attention_probs_dropout_prob							,    max_position_embeddings=self.max_position_embeddings							,    type_vocab_size=self.type_vocab_size							,    is_decoder=snake_case__							,    initializer_range=self.initializer_range							,    )

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      Any							,    snake_case__ :      Tuple							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Dict							,    snake_case__ :      List[str]							,    snake_case__ :      int							,    snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerModel(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    token_type_ids=snake_case__    )
     SCREAMING_SNAKE_CASE								=			model(snake_case__    )
     self.parent.assertEqual(result.last_hidden_state.shape							,    (self.batch_size, self.seq_length, self.hidden_size)    )

    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      str							,    snake_case__ :      Dict							,    snake_case__ :      Dict							,    snake_case__ :      Dict							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerForMaskedLM(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    labels=snake_case__    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.vocab_size)    )

    def UpperCamelCase			(			self :      Union[str, Any]							,    snake_case__ :      Tuple							,    snake_case__ :      Optional[Any]							,    snake_case__ :      int							,    snake_case__ :      Optional[int]							,    snake_case__ :      str							,    snake_case__ :      List[str]							,    snake_case__ :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerForQuestionAnswering(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    start_positions=snake_case__							,    end_positions=snake_case__							,    )
     self.parent.assertEqual(result.start_logits.shape							,    (self.batch_size, self.seq_length)    )
     self.parent.assertEqual(result.end_logits.shape							,    (self.batch_size, self.seq_length)    )

    def UpperCamelCase			(			self :      List[str]							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      int							,    snake_case__ :      str							,    snake_case__ :      Optional[int]							,    snake_case__ :      Any							,    snake_case__ :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_labels
     SCREAMING_SNAKE_CASE								=			NystromformerForSequenceClassification(snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    labels=snake_case__    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_labels)    )

    def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      int							,    snake_case__ :      Tuple							,    snake_case__ :      List[str]							,    snake_case__ :      List[Any]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      List[Any]							,    snake_case__ :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_labels
     SCREAMING_SNAKE_CASE								=			NystromformerForTokenClassification(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			model(snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    labels=snake_case__    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.num_labels)    )

    def UpperCamelCase			(			self :      int							,    snake_case__ :      Optional[Any]							,    snake_case__ :      List[str]							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Tuple							,    snake_case__ :      Optional[int]							,    snake_case__ :      Any							,    snake_case__ :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.num_choices
     SCREAMING_SNAKE_CASE								=			NystromformerForMultipleChoice(config=snake_case__    )
     model.to(snake_case__    )
     model.eval()
     SCREAMING_SNAKE_CASE								=			input_ids.unsqueeze(1    ).expand(-1							,    self.num_choices							,    -1    ).contiguous()
     SCREAMING_SNAKE_CASE								=			token_type_ids.unsqueeze(1    ).expand(-1							,    self.num_choices							,    -1    ).contiguous()
     SCREAMING_SNAKE_CASE								=			input_mask.unsqueeze(1    ).expand(-1							,    self.num_choices							,    -1    ).contiguous()
     SCREAMING_SNAKE_CASE								=			model(
         snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    labels=snake_case__							,    )
     self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.num_choices)    )




    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	(
         SCREAMING_SNAKE_CASE
     )						,	
     )								=			config_and_inputs
     SCREAMING_SNAKE_CASE								=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
     return config, inputs_dict




@require_torch
class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =(
        (
            NystromformerModel,
            NystromformerForMaskedLM,
            NystromformerForMultipleChoice,
            NystromformerForQuestionAnswering,
            NystromformerForSequenceClassification,
            NystromformerForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    __UpperCamelCase         =(
        {
            "feature-extraction": NystromformerModel,
            "fill-mask": NystromformerForMaskedLM,
            "question-answering": NystromformerForQuestionAnswering,
            "text-classification": NystromformerForSequenceClassification,
            "token-classification": NystromformerForTokenClassification,
            "zero-shot": NystromformerForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    __UpperCamelCase         =False
    __UpperCamelCase         =False

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerModelTester(self    )
     SCREAMING_SNAKE_CASE								=			ConfigTester(self							,    config_class=snake_case__							,    hidden_size=3_7    )

    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     self.config_tester.run_common_tests()

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
      SCREAMING_SNAKE_CASE								=			type
      self.model_tester.create_and_check_model(*snake_case__    )

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*snake_case__    )

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*snake_case__    )

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*snake_case__    )

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*snake_case__    )

    def UpperCamelCase			(			self :      List[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*snake_case__    )




    @slow
    def UpperCamelCase			(			self :      List[str]    ):

     """simple docstring"""






     for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      SCREAMING_SNAKE_CASE								=			NystromformerModel.from_pretrained(snake_case__    )
      self.assertIsNotNone(snake_case__    )




@require_torch
class      UpperCamelCase (     unittest.TestCase   ):

    @slow
    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			NystromformerModel.from_pretrained('uw-madison/nystromformer-512'    )
     SCREAMING_SNAKE_CASE								=			torch.tensor([[0, 1, 2, 3, 4, 5]]    )

     with torch.no_grad():
      SCREAMING_SNAKE_CASE								=			model(snake_case__    )[0]

     SCREAMING_SNAKE_CASE								=			torch.Size((1, 6, 7_6_8)    )
     self.assertEqual(output.shape							,    snake_case__    )

     SCREAMING_SNAKE_CASE								=			torch.tensor(
         [[[-0.4_532, -0.0_936, 0.5_137], [-0.2_676, 0.0_628, 0.6_186], [-0.3_629, -0.1_726, 0.4_716]]]    )

     self.assertTrue(torch.allclose(output[:, :3, :3]							,    snake_case__							,    atol=1E-4    )    )




    @slow
    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'the [MASK] of Belgium is Brussels'

     SCREAMING_SNAKE_CASE								=			AutoTokenizer.from_pretrained('uw-madison/nystromformer-512'    )
     SCREAMING_SNAKE_CASE								=			NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512'    )

     SCREAMING_SNAKE_CASE								=			tokenizer(snake_case__							,    return_tensors='pt'    )

     with torch.no_grad():
      SCREAMING_SNAKE_CASE								=			model(encoding.input_ids    ).logits

     SCREAMING_SNAKE_CASE								=			token_logits[:, 2, :].argmax(-1    )[0]

     self.assertEqual(tokenizer.decode(snake_case__    )							,    'capital'    )

