import tempfile
import unittest

from transformers import DPRConfig, is_torch_available
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 DPRContextEncoder, DPRQuestionEncoder, DPRReader, DPRReaderTokenizer
      from transformers.models.dpr.modeling_dpr import (
          DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
      )





class   lowerCAmelCase    :



     def __init__(       self					,   lowerCAmelCase__					,   lowerCAmelCase__=13					,   lowerCAmelCase__=7					,   lowerCAmelCase__=False					,   lowerCAmelCase__=True					,   lowerCAmelCase__=True					,   lowerCAmelCase__=True					,   lowerCAmelCase__=99					,   lowerCAmelCase__=32					,   lowerCAmelCase__=5					,   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					,   lowerCAmelCase__=0					,   ):
            _A=						parent
            _A=						batch_size
            _A=						seq_length
            _A=						is_training
            _A=						use_input_mask
            _A=						use_token_type_ids
            _A=						use_labels
            _A=						vocab_size
            _A=						hidden_size
            _A=						num_hidden_layers
            _A=						num_attention_heads
            _A=						intermediate_size
            _A=						hidden_act
            _A=						hidden_dropout_prob
            _A=						attention_probs_dropout_prob
            _A=						max_position_embeddings
            _A=						type_vocab_size
            _A=						type_sequence_label_size
            _A=						initializer_range
            _A=						num_labels
            _A=						num_choices
            _A=						scope
            _A=						projection_dim



     def       a__	(       self							):
            _A=						ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size							)

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

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

            _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]					,   self.num_choices							)

            _A=						self.get_config()

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



     def       a__	(       self							):
            return DPRConfig(
                projection_dim=self.projection_dim					,   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					,   )



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__							):
            _A=						DPRContextEncoder(config=_SCREAMING_SNAKE_CASE							)
            model.to(_SCREAMING_SNAKE_CASE							)
            model.eval()
            _A=						model(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   token_type_ids=_SCREAMING_SNAKE_CASE							)
            _A=						model(_SCREAMING_SNAKE_CASE					,   token_type_ids=_SCREAMING_SNAKE_CASE							)
            _A=						model(_SCREAMING_SNAKE_CASE							)
            self.parent.assertEqual(result.pooler_output.shape					,   (self.batch_size, self.projection_dim or self.hidden_size)							)



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__							):
            _A=						DPRQuestionEncoder(config=_SCREAMING_SNAKE_CASE							)
            model.to(_SCREAMING_SNAKE_CASE							)
            model.eval()
            _A=						model(_SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_SNAKE_CASE					,   token_type_ids=_SCREAMING_SNAKE_CASE							)
            _A=						model(_SCREAMING_SNAKE_CASE					,   token_type_ids=_SCREAMING_SNAKE_CASE							)
            _A=						model(_SCREAMING_SNAKE_CASE							)
            self.parent.assertEqual(result.pooler_output.shape					,   (self.batch_size, self.projection_dim or self.hidden_size)							)



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__							):
            _A=						DPRReader(config=_SCREAMING_SNAKE_CASE							)
            model.to(_SCREAMING_SNAKE_CASE							)
            model.eval()
            _A=						model(
                _SCREAMING_SNAKE_CASE					,   attention_mask=_SCREAMING_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)							)
            self.parent.assertEqual(result.relevance_logits.shape					,   (self.batch_size,)							)





     def       a__	(       self							):
            _A=						self.prepare_config_and_inputs()
            (
                (
                _A 
            ),      (
                _A 
            ),      (
                _A 
            ),      (
                _A 
            ),      (
                _A 
            ),      (
                _A 
            ),      (
                _A 
            ),       
            )=						config_and_inputs
            _A=						{'input_ids': input_ids}
            return config, inputs_dict





@require_torch
class   lowerCAmelCase    (       _SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		unittest.TestCase      ):
     _SCREAMING_SNAKE_CASE  :  Optional[Any]		=(
         (
             DPRContextEncoder,
             DPRQuestionEncoder,
             DPRReader,
         )
         if is_torch_available()
         else ()
     )
     _SCREAMING_SNAKE_CASE  :  Optional[int]		={"""feature-extraction""": DPRQuestionEncoder} if is_torch_available() else {}

     _SCREAMING_SNAKE_CASE  :  Union[str, Any]		=False
     _SCREAMING_SNAKE_CASE  :  Union[str, Any]		=False  # why?
     _SCREAMING_SNAKE_CASE  :  str		=False
     _SCREAMING_SNAKE_CASE  :  Optional[int]		=False



     def       a__	(       self							):
            _A=						DPRModelTester(self							)
            _A=						ConfigTester(self					,   config_class=_SCREAMING_SNAKE_CASE					,   hidden_size=37							)



     def       a__	(       self							):
            self.config_tester.run_common_tests()



     def       a__	(       self							):
            _A=						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_context_encoder(*_SCREAMING_SNAKE_CASE							)



     def       a__	(       self							):
            _A=						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_question_encoder(*_SCREAMING_SNAKE_CASE							)



     def       a__	(       self							):
            _A=						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_reader(*_SCREAMING_SNAKE_CASE							)



     def       a__	(       self							):
            _A=						self.model_tester.prepare_config_and_inputs()[0]

            _A=						DPRQuestionEncoder(config=_SCREAMING_SNAKE_CASE							)
            model.to(_SCREAMING_SNAKE_CASE							)
            model.eval()

            with tempfile.TemporaryDirectory() as tmp_dirname:
                   model.save_pretrained(_SCREAMING_SNAKE_CASE							)
                   _A=						DPRQuestionEncoder.from_pretrained(_SCREAMING_SNAKE_CASE					,   projection_dim=512							)

            self.assertIsNotNone(_SCREAMING_SNAKE_CASE							)





     @slow
     def       a__	(       self							):
            for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A=						DPRContextEncoder.from_pretrained(_SCREAMING_SNAKE_CASE							)
                   self.assertIsNotNone(_SCREAMING_SNAKE_CASE							)

            for model_name in DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A=						DPRContextEncoder.from_pretrained(_SCREAMING_SNAKE_CASE							)
                   self.assertIsNotNone(_SCREAMING_SNAKE_CASE							)

            for model_name in DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A=						DPRQuestionEncoder.from_pretrained(_SCREAMING_SNAKE_CASE							)
                   self.assertIsNotNone(_SCREAMING_SNAKE_CASE							)

            for model_name in DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _A=						DPRReader.from_pretrained(_SCREAMING_SNAKE_CASE							)
                   self.assertIsNotNone(_SCREAMING_SNAKE_CASE							)





@require_torch
class   lowerCAmelCase    (       unittest.TestCase      ):



     @slow
     def       a__	(       self							):
            _A=						DPRQuestionEncoder.from_pretrained('facebook/dpr-question_encoder-single-nq-base'					,   return_dict=_SCREAMING_SNAKE_CASE							)
            model.to(_SCREAMING_SNAKE_CASE							)

            _A=						torch.tensor(
                [[101, 7592, 1010, 2003, 2026, 3899, 10140, 1029, 102]]					,   dtype=torch.long					,   device=_SCREAMING_SNAKE_CASE							)  # [CLS] hello, is my dog cute? [SEP]
            _A=						model(_SCREAMING_SNAKE_CASE							)[0]  # embedding shape = (1, 768)
            # compare the actual values for a slice.
            _A=						torch.tensor(
                [
                    [
                        0.03236253,
                        0.12753335,
                        0.16818509,
                        0.00279786,
                        0.3896933,
                        0.24264945,
                        0.2178971,
                        -0.02335227,
                        -0.08481959,
                        -0.14324117,
                    ]
                ]					,   dtype=torch.float					,   device=_SCREAMING_SNAKE_CASE					,   )
            self.assertTrue(torch.allclose(output[:, :10]					,   _SCREAMING_SNAKE_CASE					,   atol=1E-4							)							)





     @slow
     def       a__	(       self							):
            _A=						DPRReaderTokenizer.from_pretrained('facebook/dpr-reader-single-nq-base'							)
            _A=						DPRReader.from_pretrained('facebook/dpr-reader-single-nq-base'							)
            model.to(_SCREAMING_SNAKE_CASE							)

            _A=						tokenizer(
                questions='What is love ?'					,   titles='Haddaway'					,   texts='What Is Love is a song recorded by the artist Haddaway'					,   padding=_SCREAMING_SNAKE_CASE					,   return_tensors='pt'					,   )
            encoded_inputs.to(_SCREAMING_SNAKE_CASE							)

            _A=						model(**_SCREAMING_SNAKE_CASE							)

            # compare the actual values for a slice.
            _A=						torch.tensor(
                [[-10.3005, -10.7765, -11.4872, -11.6841, -11.9312, -10.3002, -9.8544, -11.7378, -12.0821, -10.2975]]					,   dtype=torch.float					,   device=_SCREAMING_SNAKE_CASE					,   )

            _A=						torch.tensor(
                [[-11.0684, -11.7041, -11.5397, -10.3465, -10.8791, -6.8443, -11.9959, -11.0364, -10.0096, -6.8405]]					,   dtype=torch.float					,   device=_SCREAMING_SNAKE_CASE					,   )
            self.assertTrue(torch.allclose(outputs.start_logits[:, :10]					,   _SCREAMING_SNAKE_CASE					,   atol=1E-4							)							)
            self.assertTrue(torch.allclose(outputs.end_logits[:, :10]					,   _SCREAMING_SNAKE_CASE					,   atol=1E-4							)							)



def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	int:



	"""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							     =       input('Enter the string: ').strip()
			_A							     =       find_minimum_partitions(s)
			print(f'Minimum number of partitions required for the \'{s}\' is {ans}')