






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

__snake_case				   =    logging.get_logger(__name__)







class      UpperCAmelCase_		(   enum.Enum					):



      """simple docstring"""
      UpperCamelCase_							:   Any 	=0
      UpperCamelCase_							:   List[str] 	=1







@add_end_docstrings(lowercase					)
class      UpperCAmelCase_		(   lowercase					):



      """simple docstring"""
      UpperCamelCase_							:   Optional[Any] 	='generated'
      def __init__(							self       ,							*SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_					)							-> str:
          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       ,							SCREAMING_SNAKE_CASE_=None       ,							SCREAMING_SNAKE_CASE_=None       ,							SCREAMING_SNAKE_CASE_=None       ,							SCREAMING_SNAKE_CASE_=None       ,							SCREAMING_SNAKE_CASE_=None       ,							SCREAMING_SNAKE_CASE_=None       ,							**SCREAMING_SNAKE_CASE_       ,							)							-> str:
          UpperCamelCase			:Dict      				=  {}
          if truncation is not None:
              UpperCamelCase			:List[str]      				=  truncation

          UpperCamelCase			:Tuple      				=  generate_kwargs

          UpperCamelCase			:List[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			:Any      				=  return_type

          if clean_up_tokenization_spaces is not None:
              UpperCamelCase			:str      				=  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			:Optional[int]      				=  stop_sequence_ids[0]

          return preprocess_params, forward_params, postprocess_params
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_					)							-> Any:
          return True
      def        UpperCAmelCase		(							self       ,							*SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_					)							-> List[Any]:
          UpperCamelCase			:List[Any]      				=  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			:Any      				=  ([prefix + arg for arg in args[0]],)
              UpperCamelCase			:str      				=  True

          elif isinstance(args[0]       ,							__lowercase					):
              UpperCamelCase			:Any      				=  (prefix + args[0],)
              UpperCamelCase			:Optional[int]      				=  False
          else:
              raise ValueError(
                  F''' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'''					)
          UpperCamelCase			:List[Any]      				=  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       ,							*SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_					)							-> Dict:
          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       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=TruncationStrategy.DO_NOT_TRUNCATE       ,							**SCREAMING_SNAKE_CASE_					)							-> Union[str, Any]:
          UpperCamelCase			:Optional[int]      				=  self._parse_and_tokenize(__lowercase       ,							truncation=__lowercase       ,							**__lowercase					)
          return inputs
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_					)							-> Any:







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

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




      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=ReturnType.TEXT       ,							SCREAMING_SNAKE_CASE_=False					)							-> Optional[int]:
          UpperCamelCase			:Tuple      				=  []
          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			:Tuple      				=  {
                      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(lowercase					)
class      UpperCAmelCase_		(   lowercase					):



      """simple docstring"""
      UpperCamelCase_							:   Any 	='summary'
      def __call__(							self       ,							*SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_					)							-> List[Any]:
          return super().__call__(*__lowercase       ,							**__lowercase					)




      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_					)							-> Any:







          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      UpperCAmelCase_		(   lowercase					):



      """simple docstring"""
      UpperCamelCase_							:   Optional[int] 	='translation'
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_					)							-> Union[str, 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       ,							*SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=TruncationStrategy.DO_NOT_TRUNCATE       ,							SCREAMING_SNAKE_CASE_=None       ,							SCREAMING_SNAKE_CASE_=None					)							-> Optional[Any]:







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




      def __call__(							self       ,							*SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_					)							-> str:
          return super().__call__(*__lowercase       ,							**__lowercase					)








'''simple docstring'''

class 	lowerCAmelCase  :







       def __init__(				self      :			List[Any]    ,   __lowercase      :			str    ,   __lowercase      :			Any    ,   __lowercase      :			str   ):







            """simple docstring"""







            __lowercase      		=name
            __lowercase      		=value
            __lowercase      		=weight







       def __repr__(				self      :			List[str]   ):







            """simple docstring"""







            return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            return self.value







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            return self.name







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







            return self.weight







       def       snake_case (				self      :			Tuple   ):







            """simple docstring"""







            return self.value / self.weight






def 					__UpperCamelCase				(						lowercase__    :	Dict,       lowercase__    :	List[str],       lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=[]
     for i in range(len(lowercase__			)			):
          menu.append(Things(name[i],       value[i],       weight[i]			)			)
     return menu

def 					__UpperCamelCase				(						lowercase__    :	List[Any],       lowercase__    :	List[Any],       lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=sorted(lowercase__,       key=lowercase__,       reverse=lowercase__			)
     __lowercase      		=[]
     __lowercase		,  __lowercase      		=0.0, 0.0
     for i in range(len(lowercase__			)			):
          if (total_cost + items_copy[i].get_weight()) <= max_cost:
               result.append(items_copy[i]			)
               total_cost += items_copy[i].get_weight()
               total_value += items_copy[i].get_value()
     return (result, total_value)

def 					__UpperCamelCase				(						):






     '''simple docstring'''







if __name__ == "__main__":
       import doctest

       doctest.testmod()


