





import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
 from ... import PreTrainedTokenizerBase, TensorType

__A							:			List[str]         =			logging.get_logger(__name__)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			'vision-encoder-decoder'
     __magic_name__   =			True




     def __init__(    self				,     **snake_case_							):
         super().__init__(**snake_case_							)
         if "encoder" not in kwargs or "decoder" not in kwargs:
             raise ValueError(
                 F"A configuraton of type {self.model_type} cannot be instantiated because "
                 F"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"							)

         _A     =							kwargs.pop('encoder'							)
         _A     =							encoder_config.pop('model_type'							)
         _A     =							kwargs.pop('decoder'							)
         _A     =							decoder_config.pop('model_type'							)

         _A     =							AutoConfig.for_model(snake_case_				,     **snake_case_							)
         _A     =							AutoConfig.for_model(snake_case_				,     **snake_case_							)
         _A     =							True




     @classmethod
     def        lowerCAmelCase__		(    cls				,     snake_case_				,     snake_case_				,     **snake_case_							):
         logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'							)
         _A     =							True
         _A     =							True

         return cls(encoder=encoder_config.to_dict()				,     decoder=decoder_config.to_dict()				,     **snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							copy.deepcopy(self.__dict__							)
         _A     =							self.encoder.to_dict()
         _A     =							self.decoder.to_dict()
         _A     =							self.__class__.model_type
         return output







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     __magic_name__   =			version.parse('1.11'	)




     @property
     def        lowerCAmelCase__		(    self							):
         return OrderedDict(
             [
                 ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
             ]							)




     @property
     def        lowerCAmelCase__		(    self							):
         return 1E-4




     @property
     def        lowerCAmelCase__		(    self							):
         return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}							)







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):
         _A     =							OrderedDict()
         _A     =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
         _A     =							{0: 'batch', 1: 'past_decoder_sequence + sequence'}
         _A     =							{0: 'batch', 1: 'encoder_sequence'}

         return common_inputs




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_ = -1				,     snake_case_ = -1				,     snake_case_ = False				,     snake_case_ = None				,     ):
         import torch

         _A     =							OrderedDict()

         _A     =							super().generate_dummy_inputs(
             snake_case_				,     batch_size=snake_case_				,     seq_length=snake_case_				,     is_pair=snake_case_				,     framework=snake_case_							)

         _A,			_A     =							dummy_input['input_ids'].shape
         _A     =							(batch, encoder_sequence, self._config.encoder_hidden_size)
         _A     =							dummy_input.pop('input_ids'							)
         _A     =							dummy_input.pop('attention_mask'							)
         _A     =							torch.zeros(snake_case_							)

         return common_inputs







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     @property
     def        lowerCAmelCase__		(    self							):
         pass




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         return VisionEncoderDecoderEncoderOnnxConfig(snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_ = "default"							):
         _A     =							encoder_config.hidden_size
         return VisionEncoderDecoderDecoderOnnxConfig(snake_case_				,     snake_case_							)












from timeit import timeit

_lowerCAmelCase				:   Tuple						       =						{
    '''MALAYALAM''': True,
    '''String''': False,
    '''rotor''': True,
    '''level''': True,
    '''A''': True,
    '''BB''': True,
    '''ABC''': False,
    '''amanaplanacanalpanama''': True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    bool:
   A_       :					List[str]        =      0
   A_       :					str        =      len(_lowerCAmelCase						) - 1
   while start_i < end_i:
      if s[start_i] == s[end_i]:
         start_i += 1
         end_i -= 1
      else:
         return False
   return True

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    bool:
   A_       :					int        =      len(_lowerCAmelCase						) // 2
   A_       :					Union[str, Any]        =      len(_lowerCAmelCase						)

   # We need to traverse till half of the length of string
   # as we can get access of the i'th last element from
   # i'th index.
   # eg: [0,1,2,3,4,5] => 4th index can be accessed
   # with the help of 1st index (i==n-i-1)
   # where n is length of string
   return all(s[i] == s[n - i - 1] for i in range(_lowerCAmelCase						)						)

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    bool:
   if len(_lowerCAmelCase						) <= 2:
      return True
   if s[0] == s[len(_lowerCAmelCase						) - 1]:
      return is_palindrome_recursive(s[1:-1]						)
   else:
      return False

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    bool:
   return s == s[::-1]

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    None:
   A_       :					int        =      f"all({name}(key) is value for key, value in test_data.items())"
   A_       :					List[str]        =      f"from __main__ import test_data, {name}"
   A_       :					str        =      500000
   A_       :					List[str]        =      timeit(stmt=_lowerCAmelCase					,					setup=_lowerCAmelCase					,					number=_lowerCAmelCase						)
   print(f"{name:<35} finished {number:,} runs in {result:.5f} seconds"						)


if __name__ == "__main__":
 for key, value in test_data.items():
  assert is_palindrome(key) is is_palindrome_recursive(key)
  assert is_palindrome(key) is is_palindrome_slice(key)
  print(F'''{key:21} {value}''')
 print('''a man a plan a canal panama''')

 # finished 500,000 runs in 0.46793 seconds
 benchmark_function('''is_palindrome_slice''')
 # finished 500,000 runs in 0.85234 seconds
 benchmark_function('''is_palindrome''')
 # finished 500,000 runs in 1.32028 seconds
 benchmark_function('''is_palindrome_recursive''')
 # finished 500,000 runs in 2.08679 seconds
 benchmark_function('''is_palindrome_traversal''')
