





'''simple docstring'''






import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
	import tensorflow as tf

	from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
	from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

__UpperCamelCase					    =	logging.get_logger(__name__)




class  _A (					enum.Enum		):
		lowercase__:   List[str]				     =   0
		lowercase__:   List[Any]				     =   1





@add_end_docstrings(__lowercase		)
class  _A (					__lowercase		):
		lowercase__:   str				     =   '''generated'''

		def __init__(   self					:   Any  ,      *__magic_name__					:   Optional[int]  ,      **__magic_name__					:   Any			)					->    Optional[Any]:


					"""simple docstring"""

					super().__init__(*__magic_name__  ,      **__magic_name__			)

					self.check_model_type(
					    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
					    if self.framework == """tf"""
					    else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING			)

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   List[str]=None  ,      __magic_name__					:   Tuple=None  ,      __magic_name__					:   Tuple=None  ,      __magic_name__					:   Any=None  ,      __magic_name__					:   int=None  ,      __magic_name__					:   Any=None  ,      **__magic_name__					:   str  ,      )					->    Any:


					"""simple docstring"""

					__snake_case					:      Any							      =     {}
					if truncation is not None:
								__snake_case					:      List[str]							      =     truncation

					__snake_case					:      Optional[Any]							      =     generate_kwargs

					__snake_case					:      Dict							      =     {}
					if return_tensors is not None and return_type is None:
								__snake_case					:      int							      =     ReturnType.TENSORS if return_tensors else ReturnType.TEXT
					if return_type is not None:
								__snake_case					:      Any							      =     return_type

					if clean_up_tokenization_spaces is not None:
								__snake_case					:      Optional[int]							      =     clean_up_tokenization_spaces

					if stop_sequence is not None:
								__snake_case					:      Tuple							      =     self.tokenizer.encode(__magic_name__  ,      add_special_tokens=__magic_name__			)
								if len(__magic_name__			) > 1:
											warnings.warn(
											    """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
											    """ the stop sequence will be used as the stop sequence string in the interim."""			)
								__snake_case					:      Optional[int]							      =     stop_sequence_ids[0]

					return preprocess_params, forward_params, postprocess_params

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   int  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    List[Any]:


					"""simple docstring"""

					return True

		def 	lowercase__     (   self					:   List[Any]  ,      *__magic_name__					:   Union[str, Any]  ,      __magic_name__					:   Optional[int]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     self.model.config.prefix if self.model.config.prefix is not None else """"""
					if isinstance(args[0]  ,      __magic_name__			):
								if self.tokenizer.pad_token_id is None:
											raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input"""			)
								__snake_case					:      List[str]							      =     ([prefix + arg for arg in args[0]],)
								__snake_case					:      Dict							      =     True

					elif isinstance(args[0]  ,      __magic_name__			):
								__snake_case					:      List[str]							      =     (prefix + args[0],)
								__snake_case					:      Union[str, Any]							      =     False
					else:
								raise ValueError(
								    f''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''			)
					__snake_case					:      Dict							      =     self.tokenizer(*__magic_name__  ,      padding=__magic_name__  ,      truncation=__magic_name__  ,      return_tensors=self.framework			)
					# This is produced by tokenizers but is an invalid generate kwargs
					if "token_type_ids" in inputs:
								del inputs["token_type_ids"]
					return inputs

		def __call__(   self					:   Any  ,      *__magic_name__					:   Optional[int]  ,      **__magic_name__					:   Dict			)					->    Optional[int]:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     super().__call__(*__magic_name__  ,      **__magic_name__			)
					if (
					    isinstance(args[0]  ,      __magic_name__			)
					    and all(isinstance(__magic_name__  ,      __magic_name__			) for el in args[0]			)
					    and all(len(__magic_name__			) == 1 for res in result			)
					):
								return [res[0] for res in result]
					return result

		def 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   str=TruncationStrategy.DO_NOT_TRUNCATE  ,      **__magic_name__					:   str			)					->    str:


					"""simple docstring"""

					__snake_case					:      Dict							      =     self._parse_and_tokenize(__magic_name__  ,      truncation=__magic_name__  ,      **__magic_name__			)
					return inputs

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   Any  ,      **__magic_name__					:   List[str]			)					->    Any:


					"""simple docstring"""

					if self.framework == "pt":
								__snake_case      ,     __snake_case					:      str							      =     model_inputs["""input_ids"""].shape
					elif self.framework == "tf":
								__snake_case      ,     __snake_case					:      Optional[int]							      =     tf.shape(model_inputs["""input_ids"""]			).numpy()

					__snake_case					:      List[str]							      =     generate_kwargs.get("""min_length"""  ,      self.model.config.min_length			)
					__snake_case					:      Optional[Any]							      =     generate_kwargs.get("""max_length"""  ,      self.model.config.max_length			)
					self.check_inputs(__magic_name__  ,      generate_kwargs["""min_length"""]  ,      generate_kwargs["""max_length"""]			)
					__snake_case					:      Union[str, Any]							      =     self.model.generate(**__magic_name__  ,      **__magic_name__			)
					__snake_case					:      Optional[int]							      =     output_ids.shape[0]
					if self.framework == "pt":
								__snake_case					:      Union[str, Any]							      =     output_ids.reshape(__magic_name__  ,      out_b // in_b  ,      *output_ids.shape[1:]			)
					elif self.framework == "tf":
								__snake_case					:      Tuple							      =     tf.reshape(__magic_name__  ,      (in_b, out_b // in_b, *output_ids.shape[1:])			)
					return {"output_ids": output_ids}

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   Union[str, Any]  ,      __magic_name__					:   Any=ReturnType.TEXT  ,      __magic_name__					:   Dict=False			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     []
					for output_ids in model_outputs["output_ids"][0]:
								if return_type == ReturnType.TENSORS:
											__snake_case					:      Optional[int]							      =     {f'''{self.return_name}_token_ids''': output_ids}
								elif return_type == ReturnType.TEXT:
											__snake_case					:      Optional[int]							      =     {
											    f'''{self.return_name}_text''': self.tokenizer.decode(
											        __magic_name__  ,      skip_special_tokens=__magic_name__  ,      clean_up_tokenization_spaces=__magic_name__  ,      )
											}
								records.append(__magic_name__			)
					return records





@add_end_docstrings(__lowercase		)
class  _A (					__lowercase		):
		lowercase__:   Optional[int]				     =   '''summary'''

		def __call__(   self					:   Tuple  ,      *__magic_name__					:   Any  ,      **__magic_name__					:   str			)					->    Any:


					"""simple docstring"""

					return super().__call__(*__magic_name__  ,      **__magic_name__			)

		def 	lowercase__     (   self					:   int  ,      __magic_name__					:   int  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    bool:


					"""simple docstring"""

					if max_length < min_length:
								logger.warning(f'''Your min_length={min_length} must be inferior than your max_length={max_length}.'''			)

					if input_length < max_length:
								logger.warning(
								    f'''Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '''
								    """a summarization task, where outputs shorter than the input are typically wanted, you might """
								    f'''consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'''			)





@add_end_docstrings(__lowercase		)
class  _A (					__lowercase		):
		lowercase__:   Union[str, Any]				     =   '''translation'''

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   int  ,      __magic_name__					:   int  ,      __magic_name__					:   int			)					->    str:


					"""simple docstring"""

					if input_length > 0.9 * max_length:
								logger.warning(
								    f'''Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '''
								    """increasing your max_length manually, e.g. translator('...', max_length=400)"""			)
					return True

		def 	lowercase__     (   self					:   Optional[Any]  ,      *__magic_name__					:   Dict  ,      __magic_name__					:   List[Any]=TruncationStrategy.DO_NOT_TRUNCATE  ,      __magic_name__					:   Any=None  ,      __magic_name__					:   List[Any]=None			)					->    Tuple:


					"""simple docstring"""

					if getattr(self.tokenizer  ,      """_build_translation_inputs"""  ,      __magic_name__			):
								return self.tokenizer._build_translation_inputs(
								    *__magic_name__  ,      return_tensors=self.framework  ,      truncation=__magic_name__  ,      src_lang=__magic_name__  ,      tgt_lang=__magic_name__			)
					else:
								return super()._parse_and_tokenize(*__magic_name__  ,      truncation=__magic_name__			)

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   Any=None  ,      __magic_name__					:   Union[str, Any]=None  ,      **__magic_name__					:   Union[str, Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case      ,     __snake_case      ,     __snake_case					:      List[Any]							      =     super()._sanitize_parameters(**__magic_name__			)
					if src_lang is not None:
								__snake_case					:      int							      =     src_lang
					if tgt_lang is not None:
								__snake_case					:      Any							      =     tgt_lang
					if src_lang is None and tgt_lang is None:
								# Backward compatibility, direct arguments use is preferred.
								__snake_case					:      Tuple							      =     kwargs.get("""task"""  ,      self.task			)
								__snake_case					:      Union[str, Any]							      =     task.split("""_"""			)
								if task and len(__magic_name__			) == 4:
											# translation, XX, to YY
											__snake_case					:      List[Any]							      =     items[1]
											__snake_case					:      str							      =     items[3]
					return preprocess_params, forward_params, postprocess_params

		def __call__(   self					:   List[Any]  ,      *__magic_name__					:   int  ,      **__magic_name__					:   Union[str, Any]			)					->    int:


					"""simple docstring"""

					return super().__call__(*__magic_name__  ,      **__magic_name__			)






from timeit import timeit

lowerCamelCase		   =			{
    '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       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =0
       _lowerCamelCase			:						Union[str, Any]            =len(SCREAMING_SNAKE_CASE__	) - 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       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       _lowerCamelCase			:						List[str]            =len(SCREAMING_SNAKE_CASE__	) // 2
       _lowerCamelCase			:						Optional[Any]            =len(SCREAMING_SNAKE_CASE__	)

       # 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(SCREAMING_SNAKE_CASE__	)	)




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       if len(SCREAMING_SNAKE_CASE__	) <= 2:
              return True
       if s[0] == s[len(SCREAMING_SNAKE_CASE__	) - 1]:
              return is_palindrome_recursive(s[1:-1]	)
       else:
              return False




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       return s == s[::-1]




def       a_ (       SCREAMING_SNAKE_CASE__   : str	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =F'''all({name}(key) is value for key, value in test_data.items())'''
       _lowerCamelCase			:						List[Any]            =F'''from __main__ import test_data, {name}'''
       _lowerCamelCase			:						Any            =500_000
       _lowerCamelCase			:						Dict            =timeit(stmt=SCREAMING_SNAKE_CASE__						,     setup=SCREAMING_SNAKE_CASE__						,     number=SCREAMING_SNAKE_CASE__	)
       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')


