


import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
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 (
           MODEL_FOR_PRETRAINING_MAPPING,
           AlbertForMaskedLM,
           AlbertForMultipleChoice,
           AlbertForPreTraining,
           AlbertForQuestionAnswering,
           AlbertForSequenceClassification,
           AlbertForTokenClassification,
           AlbertModel,
       )
       from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST





class    lowerCamelCase__						:
      def __init__(		self	:	List[str]      ,       A_	:	Dict      ,       A_	:	Dict=1_3      ,       A_	:	Dict=7      ,       A_	:	Optional[int]=True      ,       A_	:	Union[str, Any]=True      ,       A_	:	Any=True      ,       A_	:	str=True      ,       A_	:	str=9_9      ,       A_	:	Dict=1_6      ,       A_	:	Dict=3_6      ,       A_	:	Dict=6      ,       A_	:	Union[str, Any]=6      ,       A_	:	str=6      ,       A_	:	Optional[int]=3_7      ,       A_	:	Tuple="gelu"      ,       A_	:	int=0.1      ,       A_	:	Any=0.1      ,       A_	:	Union[str, Any]=5_1_2      ,       A_	:	Union[str, Any]=1_6      ,       A_	:	Optional[int]=2      ,       A_	:	Optional[int]=0.02      ,       A_	:	List[Any]=3      ,       A_	:	str=4      ,       A_	:	int=None      ,       ):






       '''simple docstring'''


       __lowercase									=	parent
       __lowercase									=	batch_size
       __lowercase									=	seq_length
       __lowercase									=	is_training
       __lowercase									=	use_input_mask
       __lowercase									=	use_token_type_ids
       __lowercase									=	use_labels
       __lowercase									=	vocab_size
       __lowercase									=	embedding_size
       __lowercase									=	hidden_size
       __lowercase									=	num_hidden_layers
       __lowercase									=	num_hidden_groups
       __lowercase									=	num_attention_heads
       __lowercase									=	intermediate_size
       __lowercase									=	hidden_act
       __lowercase									=	hidden_dropout_prob
       __lowercase									=	attention_probs_dropout_prob
       __lowercase									=	max_position_embeddings
       __lowercase									=	type_vocab_size
       __lowercase									=	type_sequence_label_size
       __lowercase									=	initializer_range
       __lowercase									=	num_labels
       __lowercase									=	num_choices
       __lowercase									=	scope
      def SCREAMING_SNAKE_CASE_	(		self	:	List[str]							):






       '''simple docstring'''


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

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

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

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

       __lowercase									=	self.get_config()

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       return AlbertConfig(
           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      ,       initializer_range=self.initializer_range      ,       num_hidden_groups=self.num_hidden_groups      ,       )
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple      ,       A_	:	List[Any]      ,       A_	:	int      ,       A_	:	Optional[int]      ,       A_	:	Optional[Any]      ,       A_	:	str      ,       A_	:	List[Any]      ,       A_	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase									=	AlbertModel(config=lowercase_							)
       model.to(lowercase_							)
       model.eval()
       __lowercase									=	model(lowercase_      ,       attention_mask=lowercase_      ,       token_type_ids=lowercase_							)
       __lowercase									=	model(lowercase_      ,       token_type_ids=lowercase_							)
       __lowercase									=	model(lowercase_							)
       self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)							)
       self.parent.assertEqual(result.pooler_output.shape      ,       (self.batch_size, self.hidden_size)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]      ,       A_	:	List[Any]      ,       A_	:	Union[str, Any]      ,       A_	:	Dict      ,       A_	:	int      ,       A_	:	List[str]      ,       A_	:	List[Any]      ,       A_	:	int							):






       '''simple docstring'''


       __lowercase									=	AlbertForPreTraining(config=lowercase_							)
       model.to(lowercase_							)
       model.eval()
       __lowercase									=	model(
           lowercase_      ,       attention_mask=lowercase_      ,       token_type_ids=lowercase_      ,       labels=lowercase_      ,       sentence_order_label=lowercase_      ,       )
       self.parent.assertEqual(result.prediction_logits.shape      ,       (self.batch_size, self.seq_length, self.vocab_size)							)
       self.parent.assertEqual(result.sop_logits.shape      ,       (self.batch_size, config.num_labels)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple      ,       A_	:	Optional[int]      ,       A_	:	Optional[int]      ,       A_	:	Optional[int]      ,       A_	:	Optional[int]      ,       A_	:	Optional[Any]      ,       A_	:	Tuple      ,       A_	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	AlbertForMaskedLM(config=lowercase_							)
       model.to(lowercase_							)
       model.eval()
       __lowercase									=	model(lowercase_      ,       attention_mask=lowercase_      ,       token_type_ids=lowercase_      ,       labels=lowercase_							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.vocab_size)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple      ,       A_	:	Dict      ,       A_	:	Tuple      ,       A_	:	List[Any]      ,       A_	:	int      ,       A_	:	List[str]      ,       A_	:	Union[str, Any]      ,       A_	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	AlbertForQuestionAnswering(config=lowercase_							)
       model.to(lowercase_							)
       model.eval()
       __lowercase									=	model(
           lowercase_      ,       attention_mask=lowercase_      ,       token_type_ids=lowercase_      ,       start_positions=lowercase_      ,       end_positions=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 SCREAMING_SNAKE_CASE_	(		self	:	Dict      ,       A_	:	Optional[Any]      ,       A_	:	List[str]      ,       A_	:	Union[str, Any]      ,       A_	:	Dict      ,       A_	:	Dict      ,       A_	:	Any      ,       A_	:	int							):






       '''simple docstring'''


       __lowercase									=	self.num_labels
       __lowercase									=	AlbertForSequenceClassification(lowercase_							)
       model.to(lowercase_							)
       model.eval()
       __lowercase									=	model(lowercase_      ,       attention_mask=lowercase_      ,       token_type_ids=lowercase_      ,       labels=lowercase_							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       A_	:	List[str]      ,       A_	:	List[str]      ,       A_	:	Optional[int]      ,       A_	:	List[Any]      ,       A_	:	Dict      ,       A_	:	List[Any]      ,       A_	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.num_labels
       __lowercase									=	AlbertForTokenClassification(config=lowercase_							)
       model.to(lowercase_							)
       model.eval()
       __lowercase									=	model(lowercase_      ,       attention_mask=lowercase_      ,       token_type_ids=lowercase_      ,       labels=lowercase_							)
       self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length, self.num_labels)							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]      ,       A_	:	List[Any]      ,       A_	:	Union[str, Any]      ,       A_	:	Any      ,       A_	:	Dict      ,       A_	:	List[str]      ,       A_	:	List[Any]      ,       A_	:	Optional[int]							):






       '''simple docstring'''


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






      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


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






@require_torch
class    lowerCamelCase__						(		snake_case__			,   snake_case__			,   unittest.TestCase    ):
      a :     Dict											=		(
          (
              AlbertModel,
              AlbertForPreTraining,
              AlbertForMaskedLM,
              AlbertForMultipleChoice,
              AlbertForSequenceClassification,
              AlbertForTokenClassification,
              AlbertForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      a :     Dict											=		(
          {
              """feature-extraction""": AlbertModel,
              """fill-mask""": AlbertForMaskedLM,
              """question-answering""": AlbertForQuestionAnswering,
              """text-classification""": AlbertForSequenceClassification,
              """token-classification""": AlbertForTokenClassification,
              """zero-shot""": AlbertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      a :     List[Any]											=		True
      def SCREAMING_SNAKE_CASE_	(		self	:	int      ,       A_	:	Optional[Any]      ,       A_	:	str      ,       A_	:	Tuple=False							):






       '''simple docstring'''


       __lowercase									=	super()._prepare_for_class(lowercase_      ,       lowercase_      ,       return_labels=lowercase_							)

       if return_labels:
        if model_class in get_values(lowercase_							):
         __lowercase									=	torch.zeros(
             (self.model_tester.batch_size, self.model_tester.seq_length)      ,       dtype=torch.long      ,       device=lowercase_							)
         __lowercase									=	torch.zeros(
             self.model_tester.batch_size      ,       dtype=torch.long      ,       device=lowercase_							)
       return inputs_dict
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	AlbertModelTester(self							)
       __lowercase									=	ConfigTester(self      ,       config_class=lowercase_      ,       hidden_size=3_7							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       self.config_tester.run_common_tests()
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowercase_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Any							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*lowercase_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	int							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*lowercase_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*lowercase_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*lowercase_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*lowercase_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[Any]							):






       '''simple docstring'''


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






      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        __lowercase									=	AlbertModel.from_pretrained(lowercase_							)
        self.assertIsNotNone(lowercase_							)






@require_torch
class    lowerCamelCase__						(		unittest.TestCase    ):
      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	str							):






       '''simple docstring'''


       __lowercase									=	AlbertModel.from_pretrained("""albert-base-v2"""							)
       __lowercase									=	torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
       __lowercase									=	torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
       with torch.no_grad():
        __lowercase									=	model(lowercase_      ,       attention_mask=lowercase_							)[0]
       __lowercase									=	torch.Size((1, 1_1, 7_6_8)							)
       self.assertEqual(output.shape      ,       lowercase_							)
       __lowercase									=	torch.tensor(
           [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]							)

       self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,       lowercase_      ,       atol=1e-4							)							)




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: str ):






 """simple docstring"""







 assert x is not None
 assert y is not None

 __lowercase									=	len(UpperCamelCase__ )
 __lowercase									=	len(UpperCamelCase__ )

 # declaring the array for storing the dp values
 __lowercase									=	[[0] * (n + 1) for _ in range(m + 1 )]  # noqa: E741

 for i in range(1				,   m + 1 ):
  for j in range(1				,   n + 1 ):
   __lowercase									=	1 if x[i - 1] == y[j - 1] else 0

   __lowercase									=	max(l[i - 1][j]				,   l[i][j - 1]				,   l[i - 1][j - 1] + match )

 __lowercase									=	""""""
 __lowercase							,		__lowercase									=	m, n
 while i > 0 and j > 0:
  __lowercase									=	1 if x[i - 1] == y[j - 1] else 0

  if l[i][j] == l[i - 1][j - 1] + match:
   if match == 1:
    __lowercase									=	x[i - 1] + seq
   i -= 1
   j -= 1
  elif l[i][j] == l[i - 1][j]:
   i -= 1
  else:
   j -= 1

 return l[m][n], seq


if __name__ == "__main__":
       UpperCAmelCase__  							="AGGTAB"
       UpperCAmelCase__  							="GXTXAYB"
       UpperCAmelCase__  							=4
       UpperCAmelCase__  							="GTAB"

       UpperCAmelCase__							,      UpperCAmelCase__  							=longest_common_subsequence(a, b)
       print("len =", ln, ", sub-sequence =", subseq)
       import doctest

       doctest.testmod()
