




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

lowerCamelCase    :		int			   =       logging.get_logger(__name__)







class   A(						enum.Enum       ):



 '''simple docstring'''


 UpperCamelCase						       =						0
 UpperCamelCase						       =						1







@add_end_docstrings(UpperCamelCase       )
class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''generated'''

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

       """simple docstring"""






       super().__init__(*A_						,							**A_  )

       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     a__			(    self :  Union[str, Any]						,							A_ :  int=None						,							A_ :  List[Any]=None						,							A_ :  Union[str, Any]=None						,							A_ :  Any=None						,							A_ :  Tuple=None						,							A_ :  str=None						,							**A_ :  Union[str, Any]						,							)       ->     str:

       """simple docstring"""






       lowerCamelCase_							=   {}
       if truncation is not None:
             lowerCamelCase_							=   truncation

       lowerCamelCase_							=   generate_kwargs

       lowerCamelCase_							=   {}
       if return_tensors is not None and return_type is None:
             lowerCamelCase_							=   ReturnType.TENSORS if return_tensors else ReturnType.TEXT
       if return_type is not None:
             lowerCamelCase_							=   return_type

       if clean_up_tokenization_spaces is not None:
             lowerCamelCase_							=   clean_up_tokenization_spaces

       if stop_sequence is not None:
             lowerCamelCase_							=   self.tokenizer.encode(A_						,							add_special_tokens=A_  )
             if len(A_  ) > 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.'  )
             lowerCamelCase_							=   stop_sequence_ids[0]

       return preprocess_params, forward_params, postprocess_params

 def     a__			(    self :  Tuple						,							A_ :  int						,							A_ :  int						,							A_ :  int  )       ->     List[Any]:

       """simple docstring"""






       return True

 def     a__			(    self :  Optional[Any]						,							*A_ :  Optional[int]						,							A_ :  Dict  )       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   self.model.config.prefix if self.model.config.prefix is not None else ''
       if isinstance(args[0]						,							A_  ):
             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'  )
             lowerCamelCase_							=   ([prefix + arg for arg in args[0]],)
             lowerCamelCase_							=   True

       elif isinstance(args[0]						,							A_  ):
             lowerCamelCase_							=   (prefix + args[0],)
             lowerCamelCase_							=   False
       else:
             raise ValueError(
                 f""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""  )
       lowerCamelCase_							=   self.tokenizer(*A_						,							padding=A_						,							truncation=A_						,							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 :  str						,							*A_ :  int						,							**A_ :  Optional[int]  )       ->     Union[str, Any]:

       """simple docstring"""






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

 def     a__			(    self :  int						,							A_ :  Tuple						,							A_ :  Optional[Any]=TruncationStrategy.DO_NOT_TRUNCATE						,							**A_ :  int  )       ->     List[str]:

       """simple docstring"""






       lowerCamelCase_							=   self._parse_and_tokenize(A_						,							truncation=A_						,							**A_  )
       return inputs

 def     a__			(    self :  List[Any]						,							A_ :  str						,							**A_ :  Optional[int]  )       ->     int:

       """simple docstring"""






       if self.framework == "pt":
             lowerCamelCase_					,    lowerCamelCase_							=   model_inputs['input_ids'].shape
       elif self.framework == "tf":
             lowerCamelCase_					,    lowerCamelCase_							=   tf.shape(model_inputs['input_ids']  ).numpy()

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





 def     a__			(    self :  str						,							A_ :  List[Any]						,							A_ :  List[Any]=ReturnType.TEXT						,							A_ :  List[Any]=False  )       ->     Tuple:

       """simple docstring"""






       lowerCamelCase_							=   []
       for output_ids in model_outputs["output_ids"][0]:
             if return_type == ReturnType.TENSORS:
                   lowerCamelCase_							=   {f"""{self.return_name}_token_ids""": output_ids}
             elif return_type == ReturnType.TEXT:
                   lowerCamelCase_							=   {
                       f"""{self.return_name}_text""": self.tokenizer.decode(
                           A_						,							skip_special_tokens=A_						,							clean_up_tokenization_spaces=A_						,							)
                   }
             records.append(A_  )
       return records







@add_end_docstrings(UpperCamelCase       )
class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''summary'''

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

       """simple docstring"""






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





 def     a__			(    self :  Any						,							A_ :  int						,							A_ :  int						,							A_ :  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(UpperCamelCase       )
class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''translation'''

 def     a__			(    self :  List[str]						,							A_ :  int						,							A_ :  int						,							A_ :  int  )       ->     Any:

       """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     a__			(    self :  Optional[Any]						,							*A_ :  List[Any]						,							A_ :  str=TruncationStrategy.DO_NOT_TRUNCATE						,							A_ :  str=None						,							A_ :  List[Any]=None  )       ->     Tuple:

       """simple docstring"""






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

 def     a__			(    self :  Optional[int]						,							A_ :  Dict=None						,							A_ :  int=None						,							**A_ :  Optional[Any]  )       ->     int:

       """simple docstring"""






       lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_							=   super()._sanitize_parameters(**A_  )
       if src_lang is not None:
             lowerCamelCase_							=   src_lang
       if tgt_lang is not None:
             lowerCamelCase_							=   tgt_lang
       if src_lang is None and tgt_lang is None:
             # Backward compatibility, direct arguments use is preferred.
             lowerCamelCase_							=   kwargs.get('task'						,							self.task  )
             lowerCamelCase_							=   task.split('_'  )
             if task and len(A_  ) == 4:
                   # translation, XX, to YY
                   lowerCamelCase_							=   items[1]
                   lowerCamelCase_							=   items[3]
       return preprocess_params, forward_params, postprocess_params





 def __call__(    self :  str						,							*A_ :  int						,							**A_ :  Any  )       ->     str:

       """simple docstring"""






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









def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str					):


      '''simple docstring'''



      if not all(char in '01' for char in bin_string					):
            raise ValueError('Non-binary value was passed to the function'					)
      if not bin_string:
            raise ValueError('Empty string was passed to the function'					)
      lowerCamelCase_							=   ''
      while len(lowercase					) % 3 != 0:
            lowerCamelCase_							=   '0' + bin_string
      lowerCamelCase_							=   [
          bin_string[index : index + 3]
          for index in range(len(lowercase					)					)
          if index % 3 == 0
      ]
      for bin_group in bin_string_in_3_list:
            lowerCamelCase_							=   0
            for index, val in enumerate(lowercase					):
                  oct_val += int(2 ** (2 - index) * int(lowercase					)					)
            oct_string += str(lowercase					)
      return oct_string


if __name__ == "__main__":
 from doctest import testmod

 testmod()



