





'''simple docstring'''

def 						UpperCamelCase    (							_lowerCamelCase : str	):
    A__    =      [0 for i in range(len(_lowerCamelCase	)	)]

    # initialize interval's left pointer and right pointer
    A__,     A__    =      0, 0

    for i in range(1						,	len(_lowerCamelCase	)	):
        # case when current index is inside the interval
        if i <= right_pointer:
            A__    =      min(right_pointer - i + 1						,	z_result[i - left_pointer]	)
            A__    =      min_edge

        while go_next(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	):
            z_result[i] += 1

        # if new index's result gives us more right interval,
        # we've to update left_pointer and right_pointer
        if i + z_result[i] - 1 > right_pointer:
            A__,     A__    =      i, i + z_result[i] - 1

    return z_result





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : list[int]						,	_lowerCamelCase : str	):
    return i + z_result[i] < len(_lowerCamelCase	) and s[z_result[i]] == s[i + z_result[i]]





def 						UpperCamelCase    (							_lowerCamelCase : str						,	_lowerCamelCase : str	):
    A__    =      0
    # concatenate 'pattern' and 'input_str' and call z_function
    # with concatenated string
    A__    =      z_function(pattern + input_str	)

    for val in z_result:
        # if value is greater then length of the pattern string
        # that means this index is starting position of substring
        # which is equal to pattern string
        if val >= len(_lowerCamelCase	):
            answer += 1

    return answer


if __name__ == "__main__":
  import doctest

  doctest.testmod()










'''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        UpperCAmelCase     :
 def __init__(    self				:str						,	lowercase_				:str						,	)->							str:
     A__    =      parent
     A__    =      13
     A__    =      7
     A__    =      True
     A__    =      True
     A__    =      False
     A__    =      True
     A__    =      99
     A__    =      32
     A__    =      2
     A__    =      4
     A__    =      37
     A__    =      "gelu"
     A__    =      0.1
     A__    =      0.1
     A__    =      5_12
     A__    =      16
     A__    =      2
     A__    =      0.0_2
     A__    =      3
     A__    =      4
     A__    =      None
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							int:
     A__    =      ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size							)

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

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

     A__    =      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 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Optional[int]						,	lowercase_				:List[str]						,	lowercase_				:Any						,	lowercase_				:Union[str, Any]						,	lowercase_				:Optional[int]						,	lowercase_				:str							)->							List[str]:
     A__    =      TFDistilBertModel(config=lowercase_							)
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}

     A__    =      model(lowercase_							)

     A__    =      [input_ids, input_mask]

     A__    =      model(lowercase_							)

     self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)							)
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:str						,	lowercase_				:Optional[Any]						,	lowercase_				:Optional[int]						,	lowercase_				:Optional[int]						,	lowercase_				:Optional[int]						,	lowercase_				:Union[str, Any]							)->							Optional[int]:
     A__    =      TFDistilBertForMaskedLM(config=lowercase_							)
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)							)
 def 					UpperCAmelCase_							(    self				:Any						,	lowercase_				:str						,	lowercase_				:str						,	lowercase_				:Optional[int]						,	lowercase_				:str						,	lowercase_				:List[Any]						,	lowercase_				:Union[str, Any]							)->							Optional[int]:
     A__    =      TFDistilBertForQuestionAnswering(config=lowercase_							)
     A__    =      {
         "input_ids": input_ids,
         "attention_mask": input_mask,
     }
     A__    =      model(lowercase_							)
     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				:Union[str, Any]						,	lowercase_				:Optional[int]						,	lowercase_				:Any						,	lowercase_				:Dict						,	lowercase_				:Tuple						,	lowercase_				:Optional[Any]						,	lowercase_				:Optional[int]							)->							Any:
     A__    =      self.num_labels
     A__    =      TFDistilBertForSequenceClassification(lowercase_							)
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)							)
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Optional[Any]						,	lowercase_				:List[Any]						,	lowercase_				:Dict						,	lowercase_				:Tuple						,	lowercase_				:int						,	lowercase_				:Union[str, Any]							)->							str:
     A__    =      self.num_choices
     A__    =      TFDistilBertForMultipleChoice(lowercase_							)
     A__    =      tf.tile(tf.expand_dims(lowercase_						,	1							)						,	(1, self.num_choices, 1)							)
     A__    =      tf.tile(tf.expand_dims(lowercase_						,	1							)						,	(1, self.num_choices, 1)							)
     A__    =      {
         "input_ids": multiple_choice_inputs_ids,
         "attention_mask": multiple_choice_input_mask,
     }
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)							)
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Any						,	lowercase_				:List[str]						,	lowercase_				:Any						,	lowercase_				:int						,	lowercase_				:List[Any]						,	lowercase_				:Tuple							)->							Tuple:
     A__    =      self.num_labels
     A__    =      TFDistilBertForTokenClassification(lowercase_							)
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)							)



 def 					UpperCAmelCase_							(    self				:Any							)->							Union[str, Any]:
     A__    =      self.prepare_config_and_inputs()
     ((A__),     (A__),     (A__),     (A__),     (A__),     (A__))    =      config_and_inputs
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     return config, inputs_dict






@require_tf
class        UpperCAmelCase     (	UpperCamelCase__      ,	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    (
     (
         TFDistilBertModel,
         TFDistilBertForMaskedLM,
         TFDistilBertForQuestionAnswering,
         TFDistilBertForSequenceClassification,
         TFDistilBertForTokenClassification,
         TFDistilBertForMultipleChoice,
     )
     if is_tf_available()
     else None
 )
 __lowercase       		=    (
     {
         """feature-extraction""": TFDistilBertModel,
         """fill-mask""": TFDistilBertForMaskedLM,
         """question-answering""": TFDistilBertForQuestionAnswering,
         """text-classification""": TFDistilBertForSequenceClassification,
         """token-classification""": TFDistilBertForTokenClassification,
         """zero-shot""": TFDistilBertForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 __lowercase       		=    False
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[Any]:
     A__    =      TFDistilBertModelTester(self							)
     A__    =      ConfigTester(self						,	config_class=lowercase_						,	dim=37							)
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     self.config_tester.run_common_tests()
 def 					UpperCAmelCase_							(    self				:int							)->							Tuple:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_model(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Optional[Any]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_masked_lm(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:str							)->							str:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_question_answering(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Dict:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Optional[int]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:str							)->							int:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_token_classification(*lowercase_							)



 @slow
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Dict:
     for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]							):
         A__    =      TFDistilBertModel.from_pretrained(lowercase_							)
         self.assertIsNotNone(lowercase_							)






@require_tf
class        UpperCAmelCase     (	unittest.TestCase    ):
 @slow
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Any:
     A__    =      TFDistilBertModel.from_pretrained("distilbert-base-uncased"							)
     A__    =      tf.constant([[0, 1, 2, 3, 4, 5]]							)
     A__    =      model(lowercase_							)[0]

     A__    =      [1, 6, 7_68]
     self.assertEqual(output.shape						,	lowercase_							)

     A__    =      tf.constant(
         [
             [
                 [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
                 [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
                 [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
             ]
         ]							)
     tf.debugging.assert_near(output[:, :3, :3]						,	lowercase_						,	atol=1E-4							)



