



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 				snake_case__:

					"""simple docstring"""






					def __init__(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     int				,       ):
								lowercase__					:   Dict													=					parent
								lowercase__					:   List[Any]													=					13
								lowercase__					:   Any													=					7
								lowercase__					:   str													=					True
								lowercase__					:   Optional[Any]													=					True
								lowercase__					:   Union[str, Any]													=					True
								lowercase__					:   Tuple													=					True
								lowercase__					:   Dict													=					True
								lowercase__					:   List[str]													=					False
								lowercase__					:   Optional[Any]													=					False
								lowercase__					:   Any													=					False
								lowercase__					:   Any													=					2
								lowercase__					:   Optional[int]													=					99
								lowercase__					:   Union[str, Any]													=					0
								lowercase__					:   Optional[int]													=					32
								lowercase__					:   Union[str, Any]													=					2
								lowercase__					:   str													=					4
								lowercase__					:   Union[str, Any]													=					0.1
								lowercase__					:   Tuple													=					0.1
								lowercase__					:   Optional[Any]													=					512
								lowercase__					:   Any													=					16
								lowercase__					:   int													=					2
								lowercase__					:   Optional[Any]													=					0.02
								lowercase__					:   Tuple													=					3
								lowercase__					:   Union[str, Any]													=					4
								lowercase__					:   Tuple													=					"last"
								lowercase__					:   Tuple													=					True
								lowercase__					:   Optional[Any]													=					None
								lowercase__					:   Dict													=					0







					def 			snake_case					(							self :     List[str]					):
								lowercase__					:   str													=					ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size					)
								lowercase__					:   List[Any]													=					random_attention_mask([self.batch_size, self.seq_length]				,       dtype=tf.floataa					)

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

								lowercase__					:   Optional[int]													=					None
								if self.use_token_type_ids:
											lowercase__					:   List[Any]													=					ids_tensor([self.batch_size, self.seq_length]				,       self.n_langs					)

								lowercase__					:   List[str]													=					None
								lowercase__					:   Optional[Any]													=					None
								lowercase__					:   Tuple													=					None
								if self.use_labels:
											lowercase__					:   Tuple													=					ids_tensor([self.batch_size]				,       self.type_sequence_label_size					)
											lowercase__					:   str													=					ids_tensor([self.batch_size, self.seq_length]				,       self.num_labels					)
											lowercase__					:   Any													=					ids_tensor([self.batch_size]				,       2				,       dtype=tf.floataa					)
											lowercase__					:   List[str]													=					ids_tensor([self.batch_size]				,       self.num_choices					)

								lowercase__					:   List[str]													=					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 			snake_case					(							self :     Any				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Dict				,       ):
								lowercase__					:   Dict													=					TFFlaubertModel(config=_snake_case					)
								lowercase__					:   Tuple													=					{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
								lowercase__					:   int													=					model(_snake_case					)

								lowercase__					:   Dict													=					[input_ids, input_mask]
								lowercase__					:   List[Any]													=					model(_snake_case					)
								self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)					)







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     List[str]				,       ):
								lowercase__					:   str													=					TFFlaubertWithLMHeadModel(_snake_case					)

								lowercase__					:   List[str]													=					{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
								lowercase__					:   Optional[int]													=					model(_snake_case					)

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







					def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     Any				,       ):
								lowercase__					:   Optional[int]													=					TFFlaubertForQuestionAnsweringSimple(_snake_case					)

								lowercase__					:   Any													=					{"input_ids": input_ids, "lengths": input_lengths}

								lowercase__					:   Optional[int]													=					model(_snake_case					)

								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 			snake_case					(							self :     int				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     Union[str, Any]				,       ):
								lowercase__					:   int													=					TFFlaubertForSequenceClassification(_snake_case					)

								lowercase__					:   Optional[int]													=					{"input_ids": input_ids, "lengths": input_lengths}

								lowercase__					:   List[Any]													=					model(_snake_case					)

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







					def 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     List[Any]				,       ):
								lowercase__					:   Tuple													=					self.num_labels
								lowercase__					:   int													=					TFFlaubertForTokenClassification(config=_snake_case					)
								lowercase__					:   List[str]													=					{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
								lowercase__					:   List[str]													=					model(_snake_case					)
								self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)					)







					def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     Any				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     Tuple				,       ):
								lowercase__					:   List[Any]													=					self.num_choices
								lowercase__					:   str													=					TFFlaubertForMultipleChoice(config=_snake_case					)
								lowercase__					:   Optional[int]													=					tf.tile(tf.expand_dims(_snake_case				,       1					)				,       (1, self.num_choices, 1)					)
								lowercase__					:   int													=					tf.tile(tf.expand_dims(_snake_case				,       1					)				,       (1, self.num_choices, 1)					)
								lowercase__					:   int													=					tf.tile(tf.expand_dims(_snake_case				,       1					)				,       (1, self.num_choices, 1)					)
								lowercase__					:   Dict													=					{
								    "input_ids": multiple_choice_inputs_ids,
								    "attention_mask": multiple_choice_input_mask,
								    "token_type_ids": multiple_choice_token_type_ids,
								}
								lowercase__					:   Dict													=					model(_snake_case					)
								self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_choices)					)







					def 			snake_case					(							self :     List[str]					):
								lowercase__					:   Optional[Any]													=					self.prepare_config_and_inputs()
								(
								    (
								    lowercase__	
								)							,							(
								    lowercase__	
								)							,							(
								    lowercase__	
								)							,							(
								    lowercase__	
								)							,							(
								    lowercase__	
								)							,							(
								    lowercase__	
								)							,							(
								    lowercase__	
								)							,							(
								    lowercase__	
								)							,							(
								    lowercase__	
								)							,								
								)					:   List[str]													=					config_and_inputs
								lowercase__					:   str													=					{
								    "input_ids": input_ids,
								    "token_type_ids": token_type_ids,
								    "langs": token_type_ids,
								    "lengths": input_lengths,
								}
								return config, inputs_dict






@require_tf
class 				snake_case__(_UpperCamelCase    ,      _UpperCamelCase    ,      unittest.TestCase      ):

					"""simple docstring"""






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







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     List[str]					):


								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 			snake_case					(							self :     Tuple					):
								lowercase__					:   str													=					TFFlaubertModelTester(self					)
								lowercase__					:   List[Any]													=					ConfigTester(self				,       config_class=_snake_case				,       emb_dim=37					)







					def 			snake_case					(							self :     Dict					):
								self.config_tester.run_common_tests()







					def 			snake_case					(							self :     List[Any]					):
								lowercase__					:   Optional[Any]													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_model(*_snake_case					)







					def 			snake_case					(							self :     Union[str, Any]					):
								lowercase__					:   Optional[Any]													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_lm_head(*_snake_case					)







					def 			snake_case					(							self :     Optional[int]					):
								lowercase__					:   Union[str, Any]													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_qa(*_snake_case					)







					def 			snake_case					(							self :     Tuple					):
								lowercase__					:   Any													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_sequence_classif(*_snake_case					)







					def 			snake_case					(							self :     List[Any]					):
								lowercase__					:   str													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_for_token_classification(*_snake_case					)







					def 			snake_case					(							self :     Union[str, Any]					):
								lowercase__					:   Any													=					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_flaubert_for_multiple_choice(*_snake_case					)







					@slow
					def 			snake_case					(							self :     Any					):


								for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowercase__					:   Tuple													=					TFFlaubertModel.from_pretrained(_snake_case					)
											self.assertIsNotNone(_snake_case					)






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

					"""simple docstring"""






					@slow
					def 			snake_case					(							self :     int					):
								lowercase__					:   List[str]													=					TFFlaubertModel.from_pretrained("jplu/tf-flaubert-small-cased"					)

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

								lowercase__					:   Dict													=					model(_snake_case					)[0]
								lowercase__					:   Dict													=					tf.TensorShape((1, 8, 512)					)
								self.assertEqual(output.shape				,       _snake_case					)
								# compare the actual values for a slice.
								lowercase__					:   Tuple													=					tf.convert_to_tensor(
								    [
								        [
								            [-1.8_768_773, -1.566_555, 0.27_072_418],
								            [-1.6_920_038, -0.5_873_505, 1.9_329_599],
								            [-2.9_563_985, -1.6_993_835, 1.7_972_052],
								        ]
								    ]				,       dtype=tf.floataa				,       )

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









from collections import Counter
from timeit import timeit


def  __lowerCamelCase (       lowerCamelCase__ = ""					,			):

				"""simple docstring"""


				return sum(c % 2 for c in Counter(input_str.replace(" "					,			""						).lower()						).values()						) < 2


def  __lowerCamelCase (       lowerCamelCase__ = ""						):

				"""simple docstring"""


				if len(lowerCamelCase__						) == 0:
								return True
				lowercase__					:   str													=					input_str.replace(" "					,			""						).lower()
				# character_freq_dict: Stores the frequency of every character in the input string
				lowercase__					:   dict[str, int]													=					{}

				for character in lower_case_input_str:
								lowercase__					:   List[Any]													=					character_freq_dict.get(lowerCamelCase__					,			0						) + 1
				lowercase__					:   List[Any]													=					0

				for character_count in character_freq_dict.values():
								if character_count % 2:
												odd_char += 1
				if odd_char > 1:
								return False
				return True


def  __lowerCamelCase (       lowerCamelCase__ = ""						):

				"""simple docstring"""


				print("\nFor string = "					,			lowerCamelCase__					,			":"						)
				print(
				    "> can_string_be_rearranged_as_palindrome_counter()"					,			"\tans ="					,			can_string_be_rearranged_as_palindrome_counter(lowerCamelCase__						)					,			"\ttime ="					,			timeit(
				        "z.can_string_be_rearranged_as_palindrome_counter(z.check_str)"					,			setup="import __main__ as z"					,			)					,			"seconds"					,			)
				print(
				    "> can_string_be_rearranged_as_palindrome()"					,			"\tans ="					,			can_string_be_rearranged_as_palindrome(lowerCamelCase__						)					,			"\ttime ="					,			timeit(
				        "z.can_string_be_rearranged_as_palindrome(z.check_str)"					,			setup="import __main__ as z"					,			)					,			"seconds"					,			)


if __name__ == "__main__":
				lowerCAmelCase__					  =  input(
				    '''Enter string to determine if it can be rearranged as a palindrome or not: '''
				).strip()
				benchmark(check_str)
				lowerCAmelCase__					  =  can_string_be_rearranged_as_palindrome_counter(check_str)
				print(f'''{check_str} can {"" if status else "not "}be rearranged as a palindrome''')




