'''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

__a:			Optional[Any]	       =       logging.get_logger(__name__)



class       UpperCAmelCase  (						enum.Enum ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   0
       SCREAMING_SNAKE_CASE 		=   1



@add_end_docstrings(a__ )
class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   "generated"

       def __init__(						self     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  Union[str, Any]:
        super().__init__(*__lowerCAmelCase     ,       **__lowerCAmelCase			)

        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 _lowerCAmelCase(						self     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None     ,       **__lowerCAmelCase     ,       )  ->  int:
        lowercase__    :   Any						= {}
        if truncation is not None:
         lowercase__    :   List[str]						= truncation

        lowercase__    :   Union[str, Any]						= generate_kwargs

        lowercase__    :   List[str]						= {}
        if return_tensors is not None and return_type is None:
         lowercase__    :   Optional[int]						= ReturnType.TENSORS if return_tensors else ReturnType.TEXT
        if return_type is not None:
         lowercase__    :   Union[str, Any]						= return_type

        if clean_up_tokenization_spaces is not None:
         lowercase__    :   Union[str, Any]						= clean_up_tokenization_spaces

        if stop_sequence is not None:
         lowercase__    :   Any						= self.tokenizer.encode(__lowerCAmelCase     ,       add_special_tokens=__lowerCAmelCase			)
         if len(__lowerCAmelCase			) > 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.'''			)
         lowercase__    :   int						= stop_sequence_ids[0]

        return preprocess_params, forward_params, postprocess_params

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  Any:
        return True

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

        elif isinstance(args[0]     ,       __lowerCAmelCase			):
         lowercase__    :   Any						= (prefix + args[0],)
         lowercase__    :   Any						= False
        else:
         raise ValueError(
             F""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""			)
        lowercase__    :   Optional[Any]						= self.tokenizer(*__lowerCAmelCase     ,       padding=__lowerCAmelCase     ,       truncation=__lowerCAmelCase     ,       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     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  Optional[int]:
        lowercase__    :   Optional[Any]						= super().__call__(*__lowerCAmelCase     ,       **__lowerCAmelCase			)
        if (
            isinstance(args[0]     ,       __lowerCAmelCase			)
            and all(isinstance(__lowerCAmelCase     ,       __lowerCAmelCase			) for el in args[0]			)
            and all(len(__lowerCAmelCase			) == 1 for res in result			)
        ):
         return [res[0] for res in result]
        return result

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=TruncationStrategy.DO_NOT_TRUNCATE     ,       **__lowerCAmelCase			)  ->  int:
        lowercase__    :   Optional[int]						= self._parse_and_tokenize(__lowerCAmelCase     ,       truncation=__lowerCAmelCase     ,       **__lowerCAmelCase			)
        return inputs

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  List[Any]:
        if self.framework == "pt":
         lowercase__			,							lowercase__    :   Union[str, Any]						= model_inputs['''input_ids'''].shape
        elif self.framework == "tf":
         lowercase__			,							lowercase__    :   Any						= tf.shape(model_inputs['''input_ids''']			).numpy()

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


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=ReturnType.TEXT     ,       __lowerCAmelCase=False			)  ->  List[Any]:
        lowercase__    :   Union[str, Any]						= []
        for output_ids in model_outputs["output_ids"][0]:
         if return_type == ReturnType.TENSORS:
          lowercase__    :   Tuple						= {F"""{self.return_name}_token_ids""": output_ids}
         elif return_type == ReturnType.TEXT:
          lowercase__    :   List[Any]						= {
              F"""{self.return_name}_text""": self.tokenizer.decode(
                  __lowerCAmelCase     ,       skip_special_tokens=__lowerCAmelCase     ,       clean_up_tokenization_spaces=__lowerCAmelCase     ,       )
          }
         records.append(__lowerCAmelCase			)
        return records



@add_end_docstrings(a__ )
class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   "summary"

       def __call__(						self     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  Tuple:
        return super().__call__(*__lowerCAmelCase     ,       **__lowerCAmelCase			)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  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(a__ )
class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   "translation"

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)  ->  int:
        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 _lowerCAmelCase(						self     ,       *__lowerCAmelCase     ,       __lowerCAmelCase=TruncationStrategy.DO_NOT_TRUNCATE     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase=None			)  ->  Any:
        if getattr(self.tokenizer     ,       '''_build_translation_inputs'''     ,       __lowerCAmelCase			):
         return self.tokenizer._build_translation_inputs(
             *__lowerCAmelCase     ,       return_tensors=self.framework     ,       truncation=__lowerCAmelCase     ,       src_lang=__lowerCAmelCase     ,       tgt_lang=__lowerCAmelCase			)
        else:
         return super()._parse_and_tokenize(*__lowerCAmelCase     ,       truncation=__lowerCAmelCase			)

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


       def __call__(						self     ,       *__lowerCAmelCase     ,       **__lowerCAmelCase			)  ->  Dict:
        return super().__call__(*__lowerCAmelCase     ,       **__lowerCAmelCase			)


'''simple docstring'''

import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size'''		,					[None, 400 * 2**20, 600 * 2**20]					)
@pytest.mark.parametrize('''input_in_memory_max_size'''		,					['''default''', 0, 100 * 2**20, 900 * 2**20]					)
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 if input_in_memory_max_size != "default":
  monkeypatch.setattr(datasets.config		,					'''IN_MEMORY_MAX_SIZE'''		,					UpperCAmelCase					)
 lowercase__    :   List[Any]						= datasets.config.IN_MEMORY_MAX_SIZE
 if input_in_memory_max_size == "default":
  assert in_memory_max_size == 0
 else:
  assert in_memory_max_size == input_in_memory_max_size
 if dataset_size and in_memory_max_size:
  lowercase__    :   str						= dataset_size < in_memory_max_size
 else:
  lowercase__    :   Optional[int]						= False
 lowercase__    :   Optional[Any]						= is_small_dataset(UpperCAmelCase					)
 assert result == expected

