

'''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

lowercase__         =      logging.get_logger(__name__)




class 	A_			(    enum.Enum							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[Any]  		=  0
     UpperCAmelCase_						:     List[str]  		=  1




@add_end_docstrings(_snake_case							)
class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[str]  		=  """generated"""


     def __init__(		self			:					Optional[Any]					,      *lowercase_			:					List[Any]					,      **lowercase_			:					List[str]	)						->     Optional[Any]:
      super().__init__(*lowercase_					,      **lowercase_	)

      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        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					str=None					,      lowercase_			:					List[str]=None					,      lowercase_			:					Any=None					,      lowercase_			:					int=None					,      lowercase_			:					Tuple=None					,      lowercase_			:					List[Any]=None					,      **lowercase_			:					str					,      )						->     List[str]:
      UpperCAmelCase	:     Union[str, Any]						 =			{}
      if truncation is not None:
       UpperCAmelCase	:     str						 =			truncation

      UpperCAmelCase	:     int						 =			generate_kwargs

      UpperCAmelCase	:     Any						 =			{}
      if return_tensors is not None and return_type is None:
       UpperCAmelCase	:     Tuple						 =			ReturnType.TENSORS if return_tensors else ReturnType.TEXT
      if return_type is not None:
       UpperCAmelCase	:     str						 =			return_type

      if clean_up_tokenization_spaces is not None:
       UpperCAmelCase	:     Optional[Any]						 =			clean_up_tokenization_spaces

      if stop_sequence is not None:
       UpperCAmelCase	:     str						 =			self.tokenizer.encode(lowercase_					,      add_special_tokens=lowercase_	)
       if len(lowercase_	) > 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.'	)
       UpperCAmelCase	:     Dict						 =			stop_sequence_ids[0]

      return preprocess_params, forward_params, postprocess_params


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					int					,      lowercase_			:					int					,      lowercase_			:					int	)						->     Optional[int]:
      return True


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      *lowercase_			:					Optional[int]					,      lowercase_			:					Any	)						->     Dict:
      UpperCAmelCase	:     str						 =			self.model.config.prefix if self.model.config.prefix is not None else ''
      if isinstance(args[0]					,      lowercase_	):
       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'	)
       UpperCAmelCase	:     List[Any]						 =			([prefix + arg for arg in args[0]],)
       UpperCAmelCase	:     Dict						 =			True

      elif isinstance(args[0]					,      lowercase_	):
       UpperCAmelCase	:     Any						 =			(prefix + args[0],)
       UpperCAmelCase	:     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`"""	)
      UpperCAmelCase	:     Tuple						 =			self.tokenizer(*lowercase_					,      padding=lowercase_					,      truncation=lowercase_					,      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			:					List[str]					,      *lowercase_			:					Optional[Any]					,      **lowercase_			:					Optional[Any]	)						->     int:
      UpperCAmelCase	:     int						 =			super().__call__(*lowercase_					,      **lowercase_	)
      if (
          isinstance(args[0]					,      lowercase_	)
          and all(isinstance(lowercase_					,      lowercase_	) for el in args[0]	)
          and all(len(lowercase_	) == 1 for res in result	)
      ):
       return [res[0] for res in result]
      return result


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					Optional[Any]					,      lowercase_			:					List[str]=TruncationStrategy.DO_NOT_TRUNCATE					,      **lowercase_			:					str	)						->     List[Any]:
      UpperCAmelCase	:     Union[str, Any]						 =			self._parse_and_tokenize(lowercase_					,      truncation=lowercase_					,      **lowercase_	)
      return inputs


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					List[Any]					,      **lowercase_			:					Dict	)						->     str:
      if self.framework == "pt":
       UpperCAmelCase	:     Tuple						 =			model_inputs['input_ids'].shape
      elif self.framework == "tf":
       UpperCAmelCase	:     Any						 =			tf.shape(model_inputs['input_ids']	).numpy()

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




     def        UpperCAmelCase_	(		self			:					Tuple					,      lowercase_			:					int					,      lowercase_			:					Dict=ReturnType.TEXT					,      lowercase_			:					str=False	)						->     Optional[Any]:
      UpperCAmelCase	:     str						 =			[]
      for output_ids in model_outputs["output_ids"][0]:
       if return_type == ReturnType.TENSORS:
        UpperCAmelCase	:     Optional[Any]						 =			{f"""{self.return_name}_token_ids""": output_ids}
       elif return_type == ReturnType.TEXT:
        UpperCAmelCase	:     str						 =			{
            f"""{self.return_name}_text""": self.tokenizer.decode(
                lowercase_					,      skip_special_tokens=lowercase_					,      clean_up_tokenization_spaces=lowercase_					,      )
        }
       records.append(lowercase_	)
      return records




@add_end_docstrings(_snake_case							)
class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[Any]  		=  """summary"""


     def __call__(		self			:					Union[str, Any]					,      *lowercase_			:					Optional[int]					,      **lowercase_			:					Optional[int]	)						->     str:
      return super().__call__(*lowercase_					,      **lowercase_	)




     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					int					,      lowercase_			:					int					,      lowercase_			:					int	)						->     bool:
      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(_snake_case							)
class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Any  		=  """translation"""


     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					int					,      lowercase_			:					int					,      lowercase_			:					int	)						->     Any:
      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        UpperCAmelCase_	(		self			:					Dict					,      *lowercase_			:					Union[str, Any]					,      lowercase_			:					Any=TruncationStrategy.DO_NOT_TRUNCATE					,      lowercase_			:					Tuple=None					,      lowercase_			:					Optional[int]=None	)						->     int:
      if getattr(self.tokenizer					,      '_build_translation_inputs'					,      lowercase_	):
       return self.tokenizer._build_translation_inputs(
           *lowercase_					,      return_tensors=self.framework					,      truncation=lowercase_					,      src_lang=lowercase_					,      tgt_lang=lowercase_	)
      else:
       return super()._parse_and_tokenize(*lowercase_					,      truncation=lowercase_	)


     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					Dict=None					,      lowercase_			:					Dict=None					,      **lowercase_			:					int	)						->     int:
      UpperCAmelCase	:     Union[str, Any]						 =			super()._sanitize_parameters(**lowercase_	)
      if src_lang is not None:
       UpperCAmelCase	:     List[str]						 =			src_lang
      if tgt_lang is not None:
       UpperCAmelCase	:     Optional[int]						 =			tgt_lang
      if src_lang is None and tgt_lang is None:
       # Backward compatibility, direct arguments use is preferred.
       UpperCAmelCase	:     Union[str, Any]						 =			kwargs.get('task'					,      self.task	)
       UpperCAmelCase	:     Dict						 =			task.split('_'	)
       if task and len(lowercase_	) == 4:
        # translation, XX, to YY
        UpperCAmelCase	:     Dict						 =			items[1]
        UpperCAmelCase	:     List[Any]						 =			items[3]
      return preprocess_params, forward_params, postprocess_params




     def __call__(		self			:					Tuple					,      *lowercase_			:					Union[str, Any]					,      **lowercase_			:					Union[str, Any]	)						->     Any:
      return super().__call__(*lowercase_					,      **lowercase_	)







'''simple docstring'''

def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if b == 0:
  return 1
 if (b % 2) == 0:
  return actual_power(UpperCAmelCase_  ,	int(b / 2     )     ) * actual_power(UpperCAmelCase_  ,	int(b / 2     )     )
 else:
  return a * actual_power(UpperCAmelCase_  ,	int(b / 2     )     ) * actual_power(UpperCAmelCase_  ,	int(b / 2     )     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if b < 0:
  return 1 / actual_power(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return actual_power(UpperCAmelCase_  ,	UpperCAmelCase_     )


if __name__ == "__main__":
 print(power(-2, -3))




