'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
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 import TF_MODEL_FOR_PRETRAINING_MAPPING
						from transformers.models.albert.modeling_tf_albert import (
						    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFAlbertForMaskedLM,
						    TFAlbertForMultipleChoice,
						    TFAlbertForPreTraining,
						    TFAlbertForQuestionAnswering,
						    TFAlbertForSequenceClassification,
						    TFAlbertForTokenClassification,
						    TFAlbertModel,
						)






class     a__:


			'''simple docstring'''






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




					"""simple docstring"""


					lowerCAmelCase        =  parent
					lowerCAmelCase        =  13
					lowerCAmelCase        =  7
					lowerCAmelCase        =  True
					lowerCAmelCase        =  True
					lowerCAmelCase        =  True
					lowerCAmelCase        =  True
					lowerCAmelCase        =  99
					lowerCAmelCase        =  16
					lowerCAmelCase        =  32
					lowerCAmelCase        =  2
					lowerCAmelCase        =  4
					lowerCAmelCase        =  37
					lowerCAmelCase        =  """gelu"""
					lowerCAmelCase        =  0.1
					lowerCAmelCase        =  0.1
					lowerCAmelCase        =  512
					lowerCAmelCase        =  16
					lowerCAmelCase        =  2
					lowerCAmelCase        =  0.02
					lowerCAmelCase        =  3
					lowerCAmelCase        =  4
					lowerCAmelCase        =  None



			def 							a_	(       self):




					"""simple docstring"""


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

					lowerCAmelCase        =  None
					if self.use_input_mask:
							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        =  AlbertConfig(
					    vocab_size=self.vocab_size	,   hidden_size=self.hidden_size	,   embedding_size=self.embedding_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	,   )

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



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  TFAlbertModel(config=__lowerCAmelCase)
					# inputs = {'input_ids': input_ids,
					#           'attention_mask': input_mask,
					#           'token_type_ids': token_type_ids}
					# sequence_output, pooled_output = model(**inputs)
					lowerCAmelCase        =  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
					lowerCAmelCase        =  model(__lowerCAmelCase)

					lowerCAmelCase        =  [input_ids, input_mask]
					lowerCAmelCase        =  model(__lowerCAmelCase)

					lowerCAmelCase        =  model(__lowerCAmelCase)

					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 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  self.num_labels
					lowerCAmelCase        =  TFAlbertForPreTraining(config=__lowerCAmelCase)
					lowerCAmelCase        =  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
					lowerCAmelCase        =  model(__lowerCAmelCase)
					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, self.num_labels))



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  TFAlbertForMaskedLM(config=__lowerCAmelCase)
					lowerCAmelCase        =  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
					lowerCAmelCase        =  model(__lowerCAmelCase)
					self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.seq_length, self.vocab_size))



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  self.num_labels
					lowerCAmelCase        =  TFAlbertForSequenceClassification(config=__lowerCAmelCase)
					lowerCAmelCase        =  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
					lowerCAmelCase        =  model(__lowerCAmelCase)
					self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.num_labels))



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  TFAlbertForQuestionAnswering(config=__lowerCAmelCase)
					lowerCAmelCase        =  {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
					lowerCAmelCase        =  model(__lowerCAmelCase)
					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 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  self.num_choices
					lowerCAmelCase        =  TFAlbertForMultipleChoice(config=__lowerCAmelCase)
					lowerCAmelCase        =  tf.tile(tf.expand_dims(__lowerCAmelCase	,   1)	,   (1, self.num_choices, 1))
					lowerCAmelCase        =  tf.tile(tf.expand_dims(__lowerCAmelCase	,   1)	,   (1, self.num_choices, 1))
					lowerCAmelCase        =  tf.tile(tf.expand_dims(__lowerCAmelCase	,   1)	,   (1, self.num_choices, 1))
					lowerCAmelCase        =  {
					    """input_ids""": multiple_choice_inputs_ids,
					    """attention_mask""": multiple_choice_input_mask,
					    """token_type_ids""": multiple_choice_token_type_ids,
					}
					lowerCAmelCase        =  model(__lowerCAmelCase)
					self.parent.assertListEqual(list(result["""logits"""].shape)	,   [self.batch_size, self.num_choices])



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  self.num_labels
					lowerCAmelCase        =  TFAlbertForTokenClassification(config=__lowerCAmelCase)
					lowerCAmelCase        =  {
					    """input_ids""": input_ids,
					    """attention_mask""": input_mask,
					    """token_type_ids""": token_type_ids,
					}
					lowerCAmelCase        =  model(__lowerCAmelCase)
					self.parent.assertListEqual(list(result["""logits"""].shape)	,   [self.batch_size, self.seq_length, self.num_labels])







			def 							a_	(       self):




					"""simple docstring"""


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







@require_tf
class     a__( lowerCAmelCase__			,						lowerCAmelCase__			,						unittest.TestCase     ):


			'''simple docstring'''






			UpperCAmelCase_      :					str								=	(
			    (
			        TFAlbertModel,
			        TFAlbertForPreTraining,
			        TFAlbertForMaskedLM,
			        TFAlbertForSequenceClassification,
			        TFAlbertForQuestionAnswering,
			        TFAlbertForTokenClassification,
			        TFAlbertForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			UpperCAmelCase_      :					Optional[Any]								=	(
			    {
			        '''feature-extraction''': TFAlbertModel,
			        '''fill-mask''': TFAlbertForMaskedLM,
			        '''question-answering''': TFAlbertForQuestionAnswering,
			        '''text-classification''': TFAlbertForSequenceClassification,
			        '''token-classification''': TFAlbertForTokenClassification,
			        '''zero-shot''': TFAlbertForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			UpperCAmelCase_      :					str								=	False
			UpperCAmelCase_      :					Dict								=	False



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase=False):




					"""simple docstring"""


					lowerCAmelCase        =  super()._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase	,   return_labels=__lowerCAmelCase)

					if return_labels:
							if model_class in get_values(__lowerCAmelCase):
									lowerCAmelCase        =  tf.zeros(self.model_tester.batch_size	,   dtype=tf.intaa)

					return inputs_dict



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  TFAlbertModelTester(self)
					lowerCAmelCase        =  ConfigTester(self	,   config_class=__lowerCAmelCase	,   hidden_size=37)



			def 							a_	(       self):




					"""simple docstring"""


					self.config_tester.run_common_tests()



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_albert_model(*__lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_albert_for_pretraining(*__lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_albert_for_masked_lm(*__lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_albert_for_multiple_choice(*__lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_albert_for_sequence_classification(*__lowerCAmelCase)



			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_albert_for_question_answering(*__lowerCAmelCase)







			@slow
			def 							a_	(       self):




					"""simple docstring"""


					for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							lowerCAmelCase        =  TFAlbertModel.from_pretrained(__lowerCAmelCase)
							self.assertIsNotNone(__lowerCAmelCase)







@require_tf
class     a__( unittest.TestCase     ):


			'''simple docstring'''






			@slow
			def 							a_	(       self):




					"""simple docstring"""


					lowerCAmelCase        =  TFAlbertForPreTraining.from_pretrained("""albert-base-v2""")
					lowerCAmelCase        =  tf.constant([[0, 1, 2, 3, 4, 5]])
					lowerCAmelCase        =  model(__lowerCAmelCase)[0]

					lowerCAmelCase        =  [1, 6, 30000]
					self.assertEqual(output.shape	,   __lowerCAmelCase)

					lowerCAmelCase        =  tf.constant(
					    [
					        [
					            [4.595668, 0.74462754, -1.818147],
					            [4.5954347, 0.7454184, -1.8188258],
					            [4.5954905, 0.7448235, -1.8182316],
					        ]
					    ])
					tf.debugging.assert_near(output[:, :3, :3]	,   __lowerCAmelCase	,   atol=1E-4)





'''simple docstring'''




from __future__ import annotations

__lowercase				   =  [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]






class     a__:


			'''simple docstring'''






			def __init__(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  -1
					lowerCAmelCase        =  -1
					lowerCAmelCase        =  [-1] * length
					lowerCAmelCase        =  None
					lowerCAmelCase        =  None







			def __repr__(       self):




					"""simple docstring"""


					return f"Node(min_value={self.minn} max_value={self.maxx})"







def     snake_case__							(      _A: list[int]			)   ->     Node | None:

		'''simple docstring'''


		lowerCAmelCase        =  Node(len(_A			)			)
		lowerCAmelCase ,					lowerCAmelCase        =  min(_A			), max(_A			)
		# Leaf node case where the node contains only one unique value
		if root.minn == root.maxx:
				return root
		lowerCAmelCase        =  (root.minn + root.maxx) // 2

		lowerCAmelCase        =  []
		lowerCAmelCase        =  []

		for index, num in enumerate(_A			):
				if num <= pivot:
						left_arr.append(_A			)
				else:
						right_arr.append(_A			)
				lowerCAmelCase        =  len(_A			)
		lowerCAmelCase        =  build_tree(_A			)
		lowerCAmelCase        =  build_tree(_A			)
		return root






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if index < 0 or node is None:
				return 0
		# Leaf node cases
		if node.minn == node.maxx:
				return index + 1 if node.minn == num else 0
		lowerCAmelCase        =  (node.minn + node.maxx) // 2
		if num <= pivot:
				# go the left subtree and map index to the left subtree
				return rank_till_index(node.left				,       _A				,       node.map_left[index] - 1			)
		else:
				# go to the right subtree and map index to the right subtree
				return rank_till_index(node.right				,       _A				,       index - node.map_left[index]			)






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if start > end:
				return 0
		lowerCAmelCase        =  rank_till_index(_A				,       _A				,       _A			)
		lowerCAmelCase        =  rank_till_index(_A				,       _A				,       start - 1			)
		return rank_till_end - rank_before_start






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if index > (end - start) or start > end or node is None:
				return -1
		# Leaf node case
		if node.minn == node.maxx:
				return node.minn
		# Number of elements in the left subtree in interval [start, end]
		lowerCAmelCase        =  node.map_left[end] - (
		    node.map_left[start - 1] if start else 0
		)
		if num_elements_in_left_tree > index:
				return quantile(
				    node.left				,       _A				,       (node.map_left[start - 1] if start else 0)				,       node.map_left[end] - 1				,       )
		else:
				return quantile(
				    node.right				,       index - num_elements_in_left_tree				,       start - (node.map_left[start - 1] if start else 0)				,       end - node.map_left[end]				,       )






def     snake_case__							(      _A: Node | None				,       _A: int				,       _A: int				,       _A: int				,       _A: int			)   ->     int:

		'''simple docstring'''


		if (
		    start > end
		    or node is None
		    or start_num > end_num
		    or node.minn > end_num
		    or node.maxx < start_num
		):
				return 0
		if start_num <= node.minn and node.maxx <= end_num:
				return end - start + 1
		lowerCAmelCase        =  range_counting(
		    node.left				,       (node.map_left[start - 1] if start else 0)				,       node.map_left[end] - 1				,       _A				,       _A				,       )
		lowerCAmelCase        =  range_counting(
		    node.right				,       start - (node.map_left[start - 1] if start else 0)				,       end - node.map_left[end]				,       _A				,       _A				,       )
		return left + right


if __name__ == "__main__":
						import doctest

						doctest.testmod()




