

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 tensorflow as tf

      from transformers import (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMModel,
          TFXLMWithLMHeadModel,
          XLMConfig,
      )

class      _a				:




      """simple docstring"""

      def __init__(      self       :							Optional[Any]	, UpperCAmelCase       :							Union[str, Any]	, ):
            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_	     =							0.02
            A_	     =							3
            A_	     =							4
            A_	     =							"last"
            A_	     =							True
            A_	     =							None
            A_	     =							0





      def 		__A  (      self       :							int  ):
            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_	     =							XLMConfig(
                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 		__A  (      self       :							List[str]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							Dict	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							int	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							Any	, ):
            A_	     =							TFXLMModel(config=UpperCAmelCase  )
            A_	     =							{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
            A_	     =							model(UpperCAmelCase  )

            A_	     =							[input_ids, input_mask]
            A_	     =							model(UpperCAmelCase  )
            self.parent.assertEqual(result.last_hidden_state.shape	, (self.batch_size, self.seq_length, self.hidden_size)  )





      def 		__A  (      self       :							Dict	, UpperCAmelCase       :							List[str]	, UpperCAmelCase       :							List[Any]	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							int	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							List[Any]	, UpperCAmelCase       :							str	, ):
            A_	     =							TFXLMWithLMHeadModel(UpperCAmelCase  )

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

            A_	     =							outputs

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





      def 		__A  (      self       :							Tuple	, UpperCAmelCase       :							str	, UpperCAmelCase       :							List[str]	, UpperCAmelCase       :							List[str]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							List[str]	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							Dict	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							int	, ):
            A_	     =							TFXLMForQuestionAnsweringSimple(UpperCAmelCase  )

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

            A_	     =							model(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)  )





      def 		__A  (      self       :							Dict	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							List[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							List[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							Union[str, Any]	, ):
            A_	     =							TFXLMForSequenceClassification(UpperCAmelCase  )

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

            A_	     =							model(UpperCAmelCase  )

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





      def 		__A  (      self       :							Tuple	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							int	, UpperCAmelCase       :							int	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							Dict	, UpperCAmelCase       :							List[str]	, ):
            A_	     =							self.num_labels
            A_	     =							TFXLMForTokenClassification(config=UpperCAmelCase  )
            A_	     =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
            A_	     =							model(UpperCAmelCase  )
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.num_labels)  )





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							str	, UpperCAmelCase       :							List[str]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							str	, UpperCAmelCase       :							List[str]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							int	, UpperCAmelCase       :							List[str]	, ):
            A_	     =							self.num_choices
            A_	     =							TFXLMForMultipleChoice(config=UpperCAmelCase  )
            A_	     =							tf.tile(tf.expand_dims(UpperCAmelCase	, 1  )	, (1, self.num_choices, 1)  )
            A_	     =							tf.tile(tf.expand_dims(UpperCAmelCase	, 1  )	, (1, self.num_choices, 1)  )
            A_	     =							tf.tile(tf.expand_dims(UpperCAmelCase	, 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(UpperCAmelCase  )
            self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.num_choices)  )







      def 		__A  (      self       :							str  ):
            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      _a				( snake_case_    ,							snake_case_    ,							unittest.TestCase							):




      """simple docstring"""

      _lowerCamelCase					: Optional[int]					=					(
          (
              TFXLMModel,
              TFXLMWithLMHeadModel,
              TFXLMForSequenceClassification,
              TFXLMForQuestionAnsweringSimple,
              TFXLMForTokenClassification,
              TFXLMForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      _lowerCamelCase					: Optional[int]					=					(
          (TFXLMWithLMHeadModel,) if is_tf_available() else ()
      )  # TODO (PVP): Check other models whether language generation is also applicable
      _lowerCamelCase					: Optional[Any]					=					(
          {
              'feature-extraction': TFXLMModel,
              'fill-mask': TFXLMWithLMHeadModel,
              'question-answering': TFXLMForQuestionAnsweringSimple,
              'text-classification': TFXLMForSequenceClassification,
              'text-generation': TFXLMWithLMHeadModel,
              'token-classification': TFXLMForTokenClassification,
              'zero-shot': TFXLMForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      _lowerCamelCase					: Dict					=					False
      _lowerCamelCase					: Dict					=					False





      def 		__A  (      self       :							int	, UpperCAmelCase       :							int	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Optional[int]	, UpperCAmelCase       :							List[Any]  ):
            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 		__A  (      self       :							Optional[Any]  ):
            A_	     =							TFXLMModelTester(self  )
            A_	     =							ConfigTester(self	, config_class=UpperCAmelCase	, emb_dim=37  )





      def 		__A  (      self       :							Any  ):
            self.config_tester.run_common_tests()





      def 		__A  (      self       :							Dict  ):
            A_	     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_model(*UpperCAmelCase  )





      def 		__A  (      self       :							Union[str, Any]  ):
            A_	     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_lm_head(*UpperCAmelCase  )





      def 		__A  (      self       :							int  ):
            A_	     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_qa(*UpperCAmelCase  )





      def 		__A  (      self       :							str  ):
            A_	     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_sequence_classif(*UpperCAmelCase  )





      def 		__A  (      self       :							Optional[int]  ):
            A_	     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_for_token_classification(*UpperCAmelCase  )





      def 		__A  (      self       :							Dict  ):
            A_	     =							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_for_multiple_choice(*UpperCAmelCase  )







      @slow
      def 		__A  (      self       :							Union[str, Any]  ):
            for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_	     =							TFXLMModel.from_pretrained(UpperCAmelCase  )
                  self.assertIsNotNone(UpperCAmelCase  )




@require_tf
class      _a				( unittest.TestCase							):




      """simple docstring"""

      @slow
      def 		__A  (      self       :							Optional[int]  ):
            A_	     =							TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"  )
            A_	     =							tf.convert_to_tensor([[14, 447]]	, dtype=tf.intaa  )  # the president
            A_	     =							[
                14,
                447,
                14,
                447,
                14,
                447,
                14,
                447,
                14,
                447,
                14,
                447,
                14,
                447,
                14,
                447,
                14,
                447,
                14,
                447,
            ]  # the president the president the president the president the president the president the president the president the president the president
            # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
            A_	     =							model.generate(UpperCAmelCase	, do_sample=UpperCAmelCase  )
            self.assertListEqual(output_ids[0].numpy().tolist()	, UpperCAmelCase  )


def 			__snake_case	(   __UpperCamelCase  :   str						):



      """simple docstring"""

      A_	     =							len(__UpperCamelCase						)
      A_	     =							[0] * length
      A_	     =							[[False for i in range(__UpperCamelCase						)] for j in range(__UpperCamelCase						)]
      for i, c in enumerate(__UpperCamelCase						):
            A_	     =							i
            for j in range(i + 1						):
                  if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
                        A_	     =							True
                        A_	     =							min(__UpperCamelCase							,0 if j == 0 else (cut[j - 1] + 1)						)
            A_	     =							mincut
      return cut[length - 1]


if __name__ == "__main__":
      __a						:Union[str, Any]            = input('Enter the string: ').strip()
      __a						:Tuple            = find_minimum_partitions(s)
      print(F"Minimum number of partitions required for the '{s}' is {ans}")