
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


lowercase__		:List[Any]   =    get_tests_dir("fixtures/test_sentencepiece.model")







@require_sentencepiece
@require_tokenizers
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       str       					=XLNetTokenizer
							lowercase_       :       Dict       					=XLNetTokenizerFast
							lowercase_       :       str       					=True
							lowercase_       :       str       					=True




							def  A__      (				self):
												super().setUp()

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




							def  A__      (				self):
												lowercase							=						'''<s>'''
												lowercase							=						1

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




							def  A__      (				self):
												lowercase							=						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(A__)   ,1_0_0_6)




							def  A__      (				self):
												self.assertEqual(self.get_tokenizer().vocab_size   ,1_0_0_0)




							def  A__      (				self):
												lowercase							=						XLNetTokenizer(A__   ,keep_accents=A__)

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

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

												lowercase							=						tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
												self.assertListEqual(
												    A__   ,[
												        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''',
												        '''.''',
												    ]   ,)
												lowercase							=						tokenizer.convert_tokens_to_ids(A__)
												self.assertListEqual(A__   ,[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])

												lowercase							=						tokenizer.convert_ids_to_tokens(A__)
												self.assertListEqual(
												    A__   ,[
												        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  A__      (				self):
												lowercase							=						XLNetTokenizer(A__   ,do_lower_case=A__)
												lowercase							=						tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
												self.assertListEqual(
												    A__   ,[
												        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  A__      (				self):
												lowercase							=						XLNetTokenizer(A__   ,do_lower_case=A__)
												lowercase							=						tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
												self.assertListEqual(
												    A__   ,[
												        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  A__      (				self):
												lowercase							=						XLNetTokenizer.from_pretrained('''xlnet-base-cased''')

												lowercase							=						tokenizer.encode('''sequence builders'''   ,add_special_tokens=A__)
												lowercase							=						tokenizer.encode('''multi-sequence build'''   ,add_special_tokens=A__)

												lowercase							=						tokenizer.build_inputs_with_special_tokens(A__)
												lowercase							=						tokenizer.build_inputs_with_special_tokens(A__   ,A__)

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





							@slow
							def  A__      (				self):
												# fmt: off
												lowercase							=						{'''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=A__   ,model_name='''xlnet-base-cased'''   ,revision='''c841166438c31ec7ca9a106dee7bb312b73ae511'''   ,)








"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.distilbert.modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertModel,
  )







class _lowerCAmelCase				:







   def __init__(	self						,	UpperCamelCase__						,	)	->		str:

       '''simple docstring'''




       snake_case    :      List[str]      					=    parent
       snake_case    :      int      					=    13
       snake_case    :      Optional[int]      					=    7
       snake_case    :      Tuple      					=    True
       snake_case    :      Optional[Any]      					=    True
       snake_case    :      Optional[Any]      					=    False
       snake_case    :      Optional[Any]      					=    True
       snake_case    :      List[Any]      					=    99
       snake_case    :      Union[str, Any]      					=    32
       snake_case    :      Union[str, Any]      					=    2
       snake_case    :      Union[str, Any]      					=    4
       snake_case    :      Optional[Any]      					=    37
       snake_case    :      str      					=    "gelu"
       snake_case    :      int      					=    0.1
       snake_case    :      Any      					=    0.1
       snake_case    :      Any      					=    512
       snake_case    :      Union[str, Any]      					=    16
       snake_case    :      List[str]      					=    2
       snake_case    :      int      					=    0.02
       snake_case    :      str      					=    3
       snake_case    :      Tuple      					=    4
       snake_case    :      Optional[Any]      					=    None







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      List[str]      					=    ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size						)

       snake_case    :      Optional[Any]      					=    None
       if self.use_input_mask:
           snake_case    :      Dict      					=    random_attention_mask([self.batch_size, self.seq_length]						)

       snake_case    :      List[Any]      					=    None
       snake_case    :      int      					=    None
       snake_case    :      Tuple      					=    None
       if self.use_labels:
           snake_case    :      str      					=    ids_tensor([self.batch_size]						,	self.type_sequence_label_size						)
           snake_case    :      int      					=    ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels						)
           snake_case    :      List[Any]      					=    ids_tensor([self.batch_size]						,	self.num_choices						)

       snake_case    :      str      					=    DistilBertConfig(
           vocab_size=self.vocab_size						,	dim=self.hidden_size						,	n_layers=self.num_hidden_layers						,	n_heads=self.num_attention_heads						,	hidden_dim=self.intermediate_size						,	hidden_act=self.hidden_act						,	dropout=self.hidden_dropout_prob						,	attention_dropout=self.attention_probs_dropout_prob						,	max_position_embeddings=self.max_position_embeddings						,	initializer_range=self.initializer_range						,	)

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







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      Optional[Any]      					=    TFDistilBertModel(config=UpperCamelCase__						)
       snake_case    :      Union[str, Any]      					=    {"input_ids": input_ids, "attention_mask": input_mask}

       snake_case    :      Union[str, Any]      					=    model(UpperCamelCase__						)

       snake_case    :      Tuple      					=    [input_ids, input_mask]

       snake_case    :      Optional[Any]      					=    model(UpperCamelCase__						)

       self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       snake_case    :      Tuple      					=    TFDistilBertForMaskedLM(config=UpperCamelCase__						)
       snake_case    :      Union[str, Any]      					=    {"input_ids": input_ids, "attention_mask": input_mask}
       snake_case    :      List[str]      					=    model(UpperCamelCase__						)
       self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		str:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    TFDistilBertForQuestionAnswering(config=UpperCamelCase__						)
       snake_case    :      Optional[Any]      					=    {
           "input_ids": input_ids,
           "attention_mask": input_mask,
       }
       snake_case    :      str      					=    model(UpperCamelCase__						)
       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  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		str:

       '''simple docstring'''




       snake_case    :      int      					=    self.num_labels
       snake_case    :      List[str]      					=    TFDistilBertForSequenceClassification(UpperCamelCase__						)
       snake_case    :      List[str]      					=    {"input_ids": input_ids, "attention_mask": input_mask}
       snake_case    :      List[str]      					=    model(UpperCamelCase__						)
       self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Any:

       '''simple docstring'''




       snake_case    :      Tuple      					=    self.num_choices
       snake_case    :      Any      					=    TFDistilBertForMultipleChoice(UpperCamelCase__						)
       snake_case    :      List[str]      					=    tf.tile(tf.expand_dims(UpperCamelCase__						,	1						)						,	(1, self.num_choices, 1)						)
       snake_case    :      Any      					=    tf.tile(tf.expand_dims(UpperCamelCase__						,	1						)						,	(1, self.num_choices, 1)						)
       snake_case    :      Optional[Any]      					=    {
           "input_ids": multiple_choice_inputs_ids,
           "attention_mask": multiple_choice_input_mask,
       }
       snake_case    :      int      					=    model(UpperCamelCase__						)
       self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)						)







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)	->		Dict:

       '''simple docstring'''




       snake_case    :      str      					=    self.num_labels
       snake_case    :      Tuple      					=    TFDistilBertForTokenClassification(UpperCamelCase__						)
       snake_case    :      Union[str, Any]      					=    {"input_ids": input_ids, "attention_mask": input_mask}
       snake_case    :      List[Any]      					=    model(UpperCamelCase__						)
       self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)						)







   def  lowerCamelCase		(	self						)	->		Optional[int]:

       '''simple docstring'''




       snake_case    :      Any      					=    self.prepare_config_and_inputs()
       ((snake_case)					,(snake_case)					,(snake_case)					,(snake_case)					,(snake_case)					,(snake_case))    :      Union[str, Any]      					=    config_and_inputs
       snake_case    :      Union[str, Any]      					=    {"input_ids": input_ids, "attention_mask": input_mask}
       return config, inputs_dict







@require_tf
class _lowerCAmelCase				(      snake_case_    ,							snake_case_    ,							unittest.TestCase	):
   __UpperCAmelCase :       List[str]							  =  (
       (
           TFDistilBertModel,
           TFDistilBertForMaskedLM,
           TFDistilBertForQuestionAnswering,
           TFDistilBertForSequenceClassification,
           TFDistilBertForTokenClassification,
           TFDistilBertForMultipleChoice,
       )
       if is_tf_available()
       else None
   )
   __UpperCAmelCase :       Optional[int]							  =  (
       {
           '''feature-extraction''': TFDistilBertModel,
           '''fill-mask''': TFDistilBertForMaskedLM,
           '''question-answering''': TFDistilBertForQuestionAnswering,
           '''text-classification''': TFDistilBertForSequenceClassification,
           '''token-classification''': TFDistilBertForTokenClassification,
           '''zero-shot''': TFDistilBertForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   __UpperCAmelCase :       Optional[int]							  =  False
   __UpperCAmelCase :       Optional[Any]							  =  False







   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       snake_case    :      int      					=    TFDistilBertModelTester(self						)
       snake_case    :      str      					=    ConfigTester(self						,	config_class=UpperCamelCase__						,	dim=37						)







   def  lowerCamelCase		(	self						)	->		Tuple:

       '''simple docstring'''




       self.config_tester.run_common_tests()







   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       snake_case    :      Union[str, Any]      					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_model(*UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       snake_case    :      int      					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_masked_lm(*UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		Union[str, Any]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_question_answering(*UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		Any:

       '''simple docstring'''




       snake_case    :      str      					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_sequence_classification(*UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		List[str]:

       '''simple docstring'''




       snake_case    :      int      					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_multiple_choice(*UpperCamelCase__						)







   def  lowerCamelCase		(	self						)	->		Optional[int]:

       '''simple docstring'''




       snake_case    :      Any      					=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_distilbert_for_token_classification(*UpperCamelCase__						)







   @slow
   def  lowerCamelCase		(	self						)	->		List[str]:

       '''simple docstring'''




       for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]						):
           snake_case    :      Optional[int]      					=    TFDistilBertModel.from_pretrained(UpperCamelCase__						)
           self.assertIsNotNone(UpperCamelCase__						)







@require_tf
class _lowerCAmelCase				(      unittest.TestCase	):







   @slow
   def  lowerCamelCase		(	self						)	->		Dict:

       '''simple docstring'''




       snake_case    :      str      					=    TFDistilBertModel.from_pretrained("distilbert-base-uncased"						)
       snake_case    :      Optional[int]      					=    tf.constant([[0, 1, 2, 3, 4, 5]]						)
       snake_case    :      Any      					=    model(UpperCamelCase__						)[0]

       snake_case    :      Dict      					=    [1, 6, 768]
       self.assertEqual(output.shape						,	UpperCamelCase__						)

       snake_case    :      str      					=    tf.constant(
           [
               [
                   [0.19261885, -0.13732955, 0.4119799],
                   [0.22150156, -0.07422661, 0.39037204],
                   [0.22756018, -0.0896414, 0.3701467],
               ]
           ]						)
       tf.debugging.assert_near(output[:, :3, :3]						,	UpperCamelCase__						,	atol=1e-4						)





