
"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, 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 as np
		import tensorflow as tf

		from transformers import (
		    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaubertConfig,
		    TFFlaubertForMultipleChoice,
		    TFFlaubertForQuestionAnsweringSimple,
		    TFFlaubertForSequenceClassification,
		    TFFlaubertForTokenClassification,
		    TFFlaubertModel,
		    TFFlaubertWithLMHeadModel,
		)



class __lowerCAmelCase					:




			"""simple docstring"""







			def __init__(					self    :       Tuple							, _snake_case    :       Any							, )   ->			Optional[int]:







								"""simple docstring"""
								A_								=    parent
								A_								=    13
								A_								=    7
								A_								=    True
								A_								=    True
								A_								=    True
								A_								=    True
								A_								=    True
								A_								=    False
								A_								=    False
								A_								=    False
								A_								=    2
								A_								=    99
								A_								=    0
								A_								=    32
								A_								=    2
								A_								=    4
								A_								=    0.1
								A_								=    0.1
								A_								=    512
								A_								=    16
								A_								=    2
								A_								=    0.0_2
								A_								=    3
								A_								=    4
								A_								=    "last"
								A_								=    True
								A_								=    None
								A_								=    0




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







								"""simple docstring"""
								A_								=    ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size						)
								A_								=    random_attention_mask([self.batch_size, self.seq_length]							, dtype=tf.floataa						)

								A_								=    None
								if self.use_input_lengths:
													A_								=    (
													    ids_tensor([self.batch_size]							, vocab_size=2						) + self.seq_length - 2
													)  # small variation of seq_length

								A_								=    None
								if self.use_token_type_ids:
													A_								=    ids_tensor([self.batch_size, self.seq_length]							, self.n_langs						)

								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]							, 2							, dtype=tf.floataa						)
													A_								=    ids_tensor([self.batch_size]							, self.num_choices						)

								A_								=    FlaubertConfig(
								    vocab_size=self.vocab_size							, n_special=self.n_special							, emb_dim=self.hidden_size							, n_layers=self.num_hidden_layers							, n_heads=self.num_attention_heads							, dropout=self.hidden_dropout_prob							, attention_dropout=self.attention_probs_dropout_prob							, gelu_activation=self.gelu_activation							, sinusoidal_embeddings=self.sinusoidal_embeddings							, asm=self.asm							, causal=self.causal							, n_langs=self.n_langs							, max_position_embeddings=self.max_position_embeddings							, initializer_range=self.initializer_range							, summary_type=self.summary_type							, use_proj=self.use_proj							, bos_token_id=self.bos_token_id							, )

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    input_lengths,
								    sequence_labels,
								    token_labels,
								    is_impossible_labels,
								    choice_labels,
								    input_mask,
								)




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       Dict							, _snake_case    :       Optional[int]							, _snake_case    :       Any							, _snake_case    :       List[Any]							, _snake_case    :       Optional[int]							, _snake_case    :       int							, _snake_case    :       int							, _snake_case    :       str							, _snake_case    :       int							, )   ->			str:







								"""simple docstring"""
								A_								=    TFFlaubertModel(config=lowercase_						)
								A_								=    {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
								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   lowerCamelCase__  (					self    :       List[str]							, _snake_case    :       int							, _snake_case    :       List[str]							, _snake_case    :       Optional[Any]							, _snake_case    :       str							, _snake_case    :       Tuple							, _snake_case    :       Tuple							, _snake_case    :       Any							, _snake_case    :       Dict							, _snake_case    :       List[Any]							, )   ->			str:







								"""simple docstring"""
								A_								=    TFFlaubertWithLMHeadModel(lowercase_						)

								A_								=    {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
								A_								=    model(lowercase_						)

								self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.vocab_size)						)




			def   lowerCamelCase__  (					self    :       str							, _snake_case    :       Dict							, _snake_case    :       Union[str, Any]							, _snake_case    :       Dict							, _snake_case    :       Any							, _snake_case    :       str							, _snake_case    :       List[str]							, _snake_case    :       List[Any]							, _snake_case    :       Union[str, Any]							, _snake_case    :       str							, )   ->			Union[str, Any]:







								"""simple docstring"""
								A_								=    TFFlaubertForQuestionAnsweringSimple(lowercase_						)

								A_								=    {"input_ids": input_ids, "lengths": input_lengths}

								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   lowerCamelCase__  (					self    :       Optional[Any]							, _snake_case    :       Any							, _snake_case    :       int							, _snake_case    :       List[Any]							, _snake_case    :       Any							, _snake_case    :       Optional[Any]							, _snake_case    :       str							, _snake_case    :       int							, _snake_case    :       int							, _snake_case    :       Dict							, )   ->			List[Any]:







								"""simple docstring"""
								A_								=    TFFlaubertForSequenceClassification(lowercase_						)

								A_								=    {"input_ids": input_ids, "lengths": input_lengths}

								A_								=    model(lowercase_						)

								self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.type_sequence_label_size)						)




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       Optional[Any]							, _snake_case    :       Any							, _snake_case    :       Any							, _snake_case    :       List[Any]							, _snake_case    :       List[Any]							, _snake_case    :       Dict							, _snake_case    :       int							, _snake_case    :       Tuple							, _snake_case    :       Union[str, Any]							, )   ->			List[Any]:







								"""simple docstring"""
								A_								=    self.num_labels
								A_								=    TFFlaubertForTokenClassification(config=lowercase_						)
								A_								=    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
								A_								=    model(lowercase_						)
								self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.seq_length, self.num_labels)						)




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       Tuple							, _snake_case    :       Optional[int]							, _snake_case    :       Any							, _snake_case    :       List[Any]							, _snake_case    :       int							, _snake_case    :       Tuple							, _snake_case    :       List[str]							, _snake_case    :       List[Any]							, _snake_case    :       Any							, )   ->			Dict:







								"""simple docstring"""
								A_								=    self.num_choices
								A_								=    TFFlaubertForMultipleChoice(config=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_								=    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,
								    "token_type_ids": multiple_choice_token_type_ids,
								}
								A_								=    model(lowercase_						)
								self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.num_choices)						)






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







								"""simple docstring"""
								A_								=    self.prepare_config_and_inputs()
								(
								    (
								    A_
								)			,     (
								    A_
								)			,     (
								    A_
								)			,     (
								    A_
								)			,     (
								    A_
								)			,     (
								    A_
								)			,     (
								    A_
								)			,     (
								    A_
								)			,     (
								    A_
								)			,     
								)								=    config_and_inputs
								A_								=    {
								    "input_ids": input_ids,
								    "token_type_ids": token_type_ids,
								    "langs": token_type_ids,
								    "lengths": input_lengths,
								}
								return config, inputs_dict





@require_tf
class __lowerCAmelCase					(       _lowerCamelCase  ,					_lowerCamelCase  ,					unittest.TestCase   ):




			"""simple docstring"""







			snake_case						   =					(
			    (
			        TFFlaubertModel,
			        TFFlaubertWithLMHeadModel,
			        TFFlaubertForSequenceClassification,
			        TFFlaubertForQuestionAnsweringSimple,
			        TFFlaubertForTokenClassification,
			        TFFlaubertForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			snake_case						   =					(
			    (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
			)  # TODO (PVP): Check other models whether language generation is also applicable
			snake_case						   =					(
			    {
			        "feature-extraction": TFFlaubertModel,
			        "fill-mask": TFFlaubertWithLMHeadModel,
			        "question-answering": TFFlaubertForQuestionAnsweringSimple,
			        "text-classification": TFFlaubertForSequenceClassification,
			        "token-classification": TFFlaubertForTokenClassification,
			        "zero-shot": TFFlaubertForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			snake_case						   =					False
			snake_case						   =					False




			def   lowerCamelCase__  (					self    :       str							, _snake_case    :       Optional[Any]							, _snake_case    :       Optional[Any]							, _snake_case    :       Union[str, Any]							, _snake_case    :       Union[str, Any]							, _snake_case    :       Optional[Any]						)   ->			List[str]:







								"""simple docstring"""
								if (
								    pipeline_test_casse_name == "QAPipelineTests"
								    and tokenizer_name is not None
								    and not tokenizer_name.endswith("Fast"						)
								):
													# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
													# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
													# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
													return True

								return False




			def   lowerCamelCase__  (					self    :       int						)   ->			str:







								"""simple docstring"""
								A_								=    TFFlaubertModelTester(self						)
								A_								=    ConfigTester(self							, config_class=lowercase_							, emb_dim=37						)




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







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




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







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_model(*lowercase_						)




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







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_lm_head(*lowercase_						)




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_qa(*lowercase_						)




			def   lowerCamelCase__  (					self    :       Tuple						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_sequence_classif(*lowercase_						)




			def   lowerCamelCase__  (					self    :       List[str]						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_for_token_classification(*lowercase_						)




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







								"""simple docstring"""
								A_								=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_for_multiple_choice(*lowercase_						)






			@slow
			def   lowerCamelCase__  (					self    :       Optional[int]						)   ->			Tuple:







								"""simple docstring"""
								for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A_								=    TFFlaubertModel.from_pretrained(lowercase_						)
													self.assertIsNotNone(lowercase_						)





@require_tf
@require_sentencepiece
@require_tokenizers
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			@slow
			def   lowerCamelCase__  (					self    :       int						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    TFFlaubertModel.from_pretrained("jplu/tf-flaubert-small-cased"						)

								A_								=    tf.convert_to_tensor(
								    [[0, 158, 735, 2_592, 1_424, 6_727, 82, 1]]							, dtype=tf.intaa							, )  # "J'aime flaubert !"

								A_								=    model(lowercase_						)[0]
								A_								=    tf.TensorShape((1, 8, 512)						)
								self.assertEqual(output.shape							, lowercase_						)
								# compare the actual values for a slice.
								A_								=    tf.convert_to_tensor(
								    [
								        [
								            [-1.8_7_6_8_7_7_3, -1.5_6_6_5_5_5, 0.2_7_0_7_2_4_1_8],
								            [-1.6_9_2_0_0_3_8, -0.5_8_7_3_5_0_5, 1.9_3_2_9_5_9_9],
								            [-2.9_5_6_3_9_8_5, -1.6_9_9_3_8_3_5, 1.7_9_7_2_0_5_2],
								        ]
								    ]							, dtype=tf.floataa							, )

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()							, expected_slice.numpy()							, atol=1e-4						)						)










'''simple docstring'''

from collections.abc import Sequence
from queue import Queue






class 		a							:


				def __init__(						self       :   List[str]			,							lowercase_       :   str			,							lowercase_       :   List[Any]			,							lowercase_       :   Dict			,							lowercase_       :   int=None			,							lowercase_       :   List[Any]=None   ):
									snake_case_		     =							start
									snake_case_		     =							end
									snake_case_		     =							val
									snake_case_		     =							(start + end) // 2
									snake_case_		     =							left
									snake_case_		     =							right


				def __repr__(						self       :   Any   ):
									return F"SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"







class 		a							:


				def __init__(						self       :   Any			,							lowercase_       :   Sequence			,							lowercase_       :   List[str]   ):
									snake_case_		     =							collection
									snake_case_		     =							function
									if self.collection:
														snake_case_		     =							self._build_tree(0			,							len(lowercase_   ) - 1   )


				def     A_					(						self       :   Any			,							lowercase_       :   Dict			,							lowercase_       :   Dict   ):
									self._update_tree(self.root			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   Tuple			,							lowercase_       :   Optional[int]   ):
									return self._query_range(self.root			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   str			,							lowercase_       :   int			,							lowercase_       :   int   ):
									if start == end:
														return SegmentTreeNode(lowercase_			,							lowercase_			,							self.collection[start]   )
									snake_case_		     =							(start + end) // 2
									snake_case_		     =							self._build_tree(lowercase_			,							lowercase_   )
									snake_case_		     =							self._build_tree(mid + 1			,							lowercase_   )
									return SegmentTreeNode(lowercase_			,							lowercase_			,							self.fn(left.val			,							right.val   )			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   Optional[int]			,							lowercase_       :   int			,							lowercase_       :   Optional[Any]			,							lowercase_       :   List[str]   ):
									if node.start == i and node.end == i:
														snake_case_		     =							val
														return
									if i <= node.mid:
														self._update_tree(node.left			,							lowercase_			,							lowercase_   )
									else:
														self._update_tree(node.right			,							lowercase_			,							lowercase_   )
									snake_case_		     =							self.fn(node.left.val			,							node.right.val   )


				def     A_					(						self       :   Tuple			,							lowercase_       :   List[Any]			,							lowercase_       :   List[Any]			,							lowercase_       :   Any   ):
									if node.start == i and node.end == j:
														return node.val

									if i <= node.mid:
														if j <= node.mid:
																			# range in left child tree
																			return self._query_range(node.left			,							lowercase_			,							lowercase_   )
														else:
																			# range in left child tree and right child tree
																			return self.fn(
																			    self._query_range(node.left			,							lowercase_			,							node.mid   )			,							self._query_range(node.right			,							node.mid + 1			,							lowercase_   )			,							)
									else:
														# range in right child tree
														return self._query_range(node.right			,							lowercase_			,							lowercase_   )


				def     A_					(						self       :   Any   ):
									if self.root is not None:
														snake_case_		     =							Queue()
														queue.put(self.root   )
														while not queue.empty():
																			snake_case_		     =							queue.get()
																			yield node

																			if node.left is not None:
																								queue.put(node.left   )

																			if node.right is not None:
																								queue.put(node.right   )


if __name__ == "__main__":
							import operator

							for fn in [operator.add, max, min]:
														print('*' * 50)
														a    :							List[Any]										=    SegmentTree([2, 1, 5, 3, 4], fn)
														for node in arr.traverse():
																					print(node)
														print()

														arr.update(1, 5)
														for node in arr.traverse():
																					print(node)
														print()

														print(arr.query_range(3, 4))  # 7
														print(arr.query_range(2, 2))  # 5
														print(arr.query_range(1, 3))  # 13
														print()

