
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 lowerCAmelCase_						(			enum.Enum	):

 '''simple docstring'''

 __snake_case												=  0
 __snake_case												=  1







@add_end_docstrings(_a	)
class lowerCAmelCase_						(			_a	):

 '''simple docstring'''

 __snake_case												=  """generated"""



 def __init__(     self		,				*_UpperCAmelCase		,				**_UpperCAmelCase ):
       super().__init__(*_UpperCAmelCase		,				**_UpperCAmelCase )

       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		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None		,				**_UpperCAmelCase		,				):
       snake_case_	=				{}
       if truncation is not None:
             snake_case_	=				truncation

       snake_case_	=				generate_kwargs

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

       if clean_up_tokenization_spaces is not None:
             snake_case_	=				clean_up_tokenization_spaces

       if stop_sequence is not None:
             snake_case_	=				self.tokenizer.encode(_UpperCAmelCase		,				add_special_tokens=_UpperCAmelCase )
             if len(_UpperCAmelCase ) > 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_	=				stop_sequence_ids[0]

       return preprocess_params, forward_params, postprocess_params



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       return True



 def 						UpperCamelCase__					(     self		,				*_UpperCAmelCase		,				_UpperCAmelCase ):
       snake_case_	=				self.model.config.prefix if self.model.config.prefix is not None else ''
       if isinstance(args[0]		,				_UpperCAmelCase ):
             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_	=				([prefix + arg for arg in args[0]],)
             snake_case_	=				True

       elif isinstance(args[0]		,				_UpperCAmelCase ):
             snake_case_	=				(prefix + args[0],)
             snake_case_	=				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_	=				self.tokenizer(*_UpperCAmelCase		,				padding=_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				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		,				*_UpperCAmelCase		,				**_UpperCAmelCase ):
       snake_case_	=				super().__call__(*_UpperCAmelCase		,				**_UpperCAmelCase )
       if (
           isinstance(args[0]		,				_UpperCAmelCase )
           and all(isinstance(_UpperCAmelCase		,				_UpperCAmelCase ) for el in args[0] )
           and all(len(_UpperCAmelCase ) == 1 for res in result )
       ):
             return [res[0] for res in result]
       return result



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=TruncationStrategy.DO_NOT_TRUNCATE		,				**_UpperCAmelCase ):
       snake_case_	=				self._parse_and_tokenize(_UpperCAmelCase		,				truncation=_UpperCAmelCase		,				**_UpperCAmelCase )
       return inputs



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				**_UpperCAmelCase ):

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

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





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=ReturnType.TEXT		,				_UpperCAmelCase=False ):
       snake_case_	=				[]
       for output_ids in model_outputs["output_ids"][0]:
             if return_type == ReturnType.TENSORS:
                   snake_case_	=				{F'''{self.return_name}_token_ids''': output_ids}
             elif return_type == ReturnType.TEXT:
                   snake_case_	=				{
                       F'''{self.return_name}_text''': self.tokenizer.decode(
                           _UpperCAmelCase		,				skip_special_tokens=_UpperCAmelCase		,				clean_up_tokenization_spaces=_UpperCAmelCase		,				)
                   }
             records.append(_UpperCAmelCase )
       return records







@add_end_docstrings(_a	)
class lowerCAmelCase_						(			_a	):

 '''simple docstring'''

 __snake_case												=  """summary"""



 def __call__(     self		,				*_UpperCAmelCase		,				**_UpperCAmelCase ):
       return super().__call__(*_UpperCAmelCase		,				**_UpperCAmelCase )





 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):

       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(_a	)
class lowerCAmelCase_						(			_a	):

 '''simple docstring'''

 __snake_case												=  """translation"""



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase ):

       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		,				*_UpperCAmelCase		,				_UpperCAmelCase=TruncationStrategy.DO_NOT_TRUNCATE		,				_UpperCAmelCase=None		,				_UpperCAmelCase=None ):

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



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





 def __call__(     self		,				*_UpperCAmelCase		,				**_UpperCAmelCase ):
       return super().__call__(*_UpperCAmelCase		,				**_UpperCAmelCase )



import os






def 	__lowerCAmelCase   ()->  List[Any]:




      """simple docstring"""

      snake_case_	=				os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE ) , '''num.txt''' )
      with open(SCREAMING_SNAKE_CASE ) as file_hand:
            return str(sum(int(SCREAMING_SNAKE_CASE ) for line in file_hand ) )[:10]


if __name__ == "__main__":
   print(solution())