





'''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

lowerCAmelCase       		=      logging.get_logger(__name__)






class 		lowerCamelCase  (      enum.Enum       ):
	snake_case_      =   0
	snake_case_      =   1






@add_end_docstrings(lowercase__       )
class 		lowerCamelCase  (      lowercase__       ):
	snake_case_      =   "generated"



	def __init__(   self		,      *a_		,      **a_	):
			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   _lowerCamelCase    (   self		,      a_=None		,      a_=None		,      a_=None		,      a_=None		,      a_=None		,      a_=None		,      **a_		,      ):
			lowerCAmelCase     :    Dict						=   {}
			if truncation is not None:
					lowerCAmelCase     :    List[Any]						=   truncation

			lowerCAmelCase     :    List[Any]						=   generate_kwargs

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

			if clean_up_tokenization_spaces is not None:
					lowerCAmelCase     :    Dict						=   clean_up_tokenization_spaces

			if stop_sequence is not None:
					lowerCAmelCase     :    Optional[Any]						=   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     :    Tuple						=   stop_sequence_ids[0]

			return preprocess_params, forward_params, postprocess_params



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_	):
			return True



	def   _lowerCamelCase    (   self		,      *a_		,      a_	):
			lowerCAmelCase     :    List[Any]						=   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     :    Tuple						=   ([prefix + arg for arg in args[0]],)
					lowerCAmelCase     :    Tuple						=   True

			elif isinstance(args[0]		,      a_	):
					lowerCAmelCase     :    Any						=   (prefix + args[0],)
					lowerCAmelCase     :    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`'''	)
			lowerCAmelCase     :    Union[str, Any]						=   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		,      *a_		,      **a_	):
			lowerCAmelCase     :    Union[str, Any]						=   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   _lowerCamelCase    (   self		,      a_		,      a_=TruncationStrategy.DO_NOT_TRUNCATE		,      **a_	):
			lowerCAmelCase     :    Union[str, Any]						=   self._parse_and_tokenize(a_		,      truncation=a_		,      **a_	)
			return inputs



	def   _lowerCamelCase    (   self		,      a_		,      **a_	):
			if self.framework == "pt":
					lowerCAmelCase     :    Union[str, Any]						=   model_inputs["input_ids"].shape
			elif self.framework == "tf":
					lowerCAmelCase     :    Dict						=   tf.shape(model_inputs["input_ids"]	).numpy()

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





	def   _lowerCamelCase    (   self		,      a_		,      a_=ReturnType.TEXT		,      a_=False	):
			lowerCAmelCase     :    List[str]						=   []
			for output_ids in model_outputs["output_ids"][0]:
					if return_type == ReturnType.TENSORS:
							lowerCAmelCase     :    Union[str, Any]						=   {F'''{self.return_name}_token_ids''': output_ids}
					elif return_type == ReturnType.TEXT:
							lowerCAmelCase     :    List[str]						=   {
							    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(lowercase__       )
class 		lowerCamelCase  (      lowercase__       ):
	snake_case_      =   "summary"



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





	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_	):
			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 		lowerCamelCase  (      lowercase__       ):
	snake_case_      =   "translation"



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_	):
			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		,      *a_		,      a_=TruncationStrategy.DO_NOT_TRUNCATE		,      a_=None		,      a_=None	):
			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   _lowerCamelCase    (   self		,      a_=None		,      a_=None		,      **a_	):
			lowerCAmelCase     :    List[str]						=   super()._sanitize_parameters(**a_	)
			if src_lang is not None:
					lowerCAmelCase     :    int						=   src_lang
			if tgt_lang is not None:
					lowerCAmelCase     :    str						=   tgt_lang
			if src_lang is None and tgt_lang is None:
					# Backward compatibility, direct arguments use is preferred.
					lowerCAmelCase     :    int						=   kwargs.get("task"		,      self.task	)
					lowerCAmelCase     :    Tuple						=   task.split("_"	)
					if task and len(a_	) == 4:
							# translation, XX, to YY
							lowerCAmelCase     :    str						=   items[1]
							lowerCAmelCase     :    Union[str, Any]						=   items[3]
			return preprocess_params, forward_params, postprocess_params





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










'''simple docstring'''






import numpy as np






def      __A						(							a_						:  np.array	):
		return 1 / (1 + np.exp(-vector	))


if __name__ == "__main__":
					import doctest

					doctest.testmod()



