


'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import 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 numpy
     import tensorflow as tf

     from transformers import (
         TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
         TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
         BertConfig,
         DPRConfig,
         TFDPRContextEncoder,
         TFDPRQuestionEncoder,
         TFDPRReader,
     )





class      snake_case    :





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase=13			,  UpperCamelCase=7			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=99			,  UpperCamelCase=32			,  UpperCamelCase=2			,  UpperCamelCase=4			,  UpperCamelCase=37			,  UpperCamelCase="gelu"			,  UpperCamelCase=0.1			,  UpperCamelCase=0.1			,  UpperCamelCase=512			,  UpperCamelCase=16			,  UpperCamelCase=2			,  UpperCamelCase=0.02			,  UpperCamelCase=3			,  UpperCamelCase=4			,  UpperCamelCase=None			,  UpperCamelCase=0			,  ):







   """simple docstring"""
   lowerCamelCase_      =  parent
   lowerCamelCase_      =  batch_size
   lowerCamelCase_      =  seq_length
   lowerCamelCase_      =  is_training
   lowerCamelCase_      =  use_input_mask
   lowerCamelCase_      =  use_token_type_ids
   lowerCamelCase_      =  use_labels
   lowerCamelCase_      =  vocab_size
   lowerCamelCase_      =  hidden_size
   lowerCamelCase_      =  num_hidden_layers
   lowerCamelCase_      =  num_attention_heads
   lowerCamelCase_      =  intermediate_size
   lowerCamelCase_      =  hidden_act
   lowerCamelCase_      =  hidden_dropout_prob
   lowerCamelCase_      =  attention_probs_dropout_prob
   lowerCamelCase_      =  max_position_embeddings
   lowerCamelCase_      =  type_vocab_size
   lowerCamelCase_      =  type_sequence_label_size
   lowerCamelCase_      =  initializer_range
   lowerCamelCase_      =  num_labels
   lowerCamelCase_      =  num_choices
   lowerCamelCase_      =  scope
   lowerCamelCase_      =  projection_dim






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size     )

   lowerCamelCase_      =  None
   if self.use_input_mask:
    # follow test_modeling_tf_ctrl.py
    lowerCamelCase_      =  random_attention_mask([self.batch_size, self.seq_length]     )

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

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

   lowerCamelCase_      =  BertConfig(
       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=UpperCamelCase			,  initializer_range=self.initializer_range			,  )
   lowerCamelCase_      =  DPRConfig(projection_dim=self.projection_dim			,  **config.to_dict()     )

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






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  TFDPRContextEncoder(config=UpperCamelCase     )
   lowerCamelCase_      =  model(UpperCamelCase			,  attention_mask=UpperCamelCase			,  token_type_ids=UpperCamelCase     )
   lowerCamelCase_      =  model(UpperCamelCase			,  token_type_ids=UpperCamelCase     )
   lowerCamelCase_      =  model(UpperCamelCase     )
   self.parent.assertEqual(result.pooler_output.shape			,  (self.batch_size, self.projection_dim or self.hidden_size)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  TFDPRQuestionEncoder(config=UpperCamelCase     )
   lowerCamelCase_      =  model(UpperCamelCase			,  attention_mask=UpperCamelCase			,  token_type_ids=UpperCamelCase     )
   lowerCamelCase_      =  model(UpperCamelCase			,  token_type_ids=UpperCamelCase     )
   lowerCamelCase_      =  model(UpperCamelCase     )
   self.parent.assertEqual(result.pooler_output.shape			,  (self.batch_size, self.projection_dim or self.hidden_size)     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  TFDPRReader(config=UpperCamelCase     )
   lowerCamelCase_      =  model(UpperCamelCase			,  attention_mask=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)     )
   self.parent.assertEqual(result.relevance_logits.shape			,  (self.batch_size,)     )







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.prepare_config_and_inputs()
   (
       (
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,(
       lowerCamelCase_
   ) ,
   )      =  config_and_inputs
   lowerCamelCase_      =  {"input_ids": input_ids}
   return config, inputs_dict





@require_tf
class      snake_case    (    lowercase			,						lowercase			,						unittest.TestCase			):





  """simple docstring"""




  _lowerCamelCase		    =  (
      (
          TFDPRContextEncoder,
          TFDPRQuestionEncoder,
          TFDPRReader,
      )
      if is_tf_available()
      else ()
  )
  _lowerCamelCase		    =  {"feature-extraction": TFDPRQuestionEncoder} if is_tf_available() else {}

  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False
  _lowerCamelCase		    =  False






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFDPRModelTester(self     )
   lowerCamelCase_      =  ConfigTester(self			,  config_class=UpperCamelCase			,  hidden_size=37     )






  def   snake_case		(    self     ):







   """simple docstring"""
   self.config_tester.run_common_tests()






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_dpr_context_encoder(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_dpr_question_encoder(*UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_dpr_reader(*UpperCamelCase     )







  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_      =  TFDPRContextEncoder.from_pretrained(UpperCamelCase     )
    self.assertIsNotNone(UpperCamelCase     )

   for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_      =  TFDPRContextEncoder.from_pretrained(UpperCamelCase     )
    self.assertIsNotNone(UpperCamelCase     )

   for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_      =  TFDPRQuestionEncoder.from_pretrained(UpperCamelCase     )
    self.assertIsNotNone(UpperCamelCase     )

   for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_      =  TFDPRReader.from_pretrained(UpperCamelCase     )
    self.assertIsNotNone(UpperCamelCase     )





@require_tf
class      snake_case    (    unittest.TestCase			):





  """simple docstring"""




  @slow
  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  TFDPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-single-nq-base"     )

   lowerCamelCase_      =  tf.constant(
       [[101, 7592, 1010, 2003, 2026, 3899, 1_0140, 1029, 102]]     )  # [CLS] hello, is my dog cute? [SEP]
   lowerCamelCase_      =  model(UpperCamelCase     )[0]  # embedding shape = (1, 768)
   # compare the actual values for a slice.
   lowerCamelCase_      =  tf.constant(
       [
           [
               0.03_236_253,
               0.12_753_335,
               0.16_818_509,
               0.00_279_786,
               0.3_896_933,
               0.24_264_945,
               0.2_178_971,
               -0.02_335_227,
               -0.08_481_959,
               -0.14_324_117,
           ]
       ]     )
   self.assertTrue(numpy.allclose(output[:, :10].numpy()			,  expected_slice.numpy()			,  atol=1e-4     )     )







from __future__ import annotations







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	bool:

		'''simple docstring'''







		__lowercase=			get_failure_array(lowercase__	)

		# 2) Step through text searching for pattern
		__lowercase,							__lowercase=			0, 0  # index into text, pattern
		while i < len(lowercase__	):
				if pattern[j] == text[i]:
						if j == (len(lowercase__	) - 1):
								return True
						j += 1

				# if this is a prefix in our pattern
				# just go back far enough to continue
				elif j > 0:
						__lowercase=			failure[j - 1]
						continue
				i += 1
		return False







def        _lowerCamelCase(	lowercase__	)						->	list[int]:

		'''simple docstring'''







		__lowercase=			[0]
		__lowercase=			0
		__lowercase=			1
		while j < len(lowercase__	):
				if pattern[i] == pattern[j]:
						i += 1
				elif i > 0:
						__lowercase=			failure[i - 1]
						continue
				j += 1
				failure.append(lowercase__	)
		return failure


if __name__ == "__main__":
	# Test 1)
	lowerCAmelCase           =		'''abc1abc12'''
	lowerCAmelCase           =		'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
	lowerCAmelCase           =		'''alskfjaldsk23adsfabcabc'''
	assert kmp(pattern, texta) and not kmp(pattern, texta)

	# Test 2)
	lowerCAmelCase           =		'''ABABX'''
	lowerCAmelCase           =		'''ABABZABABYABABX'''
	assert kmp(pattern, text)

	# Test 3)
	lowerCAmelCase           =		'''AAAB'''
	lowerCAmelCase           =		'''ABAAAAAB'''
	assert kmp(pattern, text)

	# Test 4)
	lowerCAmelCase           =		'''abcdabcy'''
	lowerCAmelCase           =		'''abcxabcdabxabcdabcdabcy'''
	assert kmp(pattern, text)

	# Test 5)
	lowerCAmelCase           =		'''aabaabaaa'''
	assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]






