

'''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_       						=							logging.get_logger(__name__)





class 							_snake_case (       enum.Enum						):
   _A       :     Any       =					0
   _A       :     int       =					1





@add_end_docstrings(_a						)
class 							_snake_case (       _a						):
   _A       :     Dict       =					'''generated'''
   def __init__( self			:      List[str]     ,*SCREAMING_SNAKE_CASE__			:      Dict     ,**SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     super().__init__(*SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)

     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			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=None     ,SCREAMING_SNAKE_CASE__			:      List[Any]=None     ,SCREAMING_SNAKE_CASE__			:      Tuple=None     ,SCREAMING_SNAKE_CASE__			:      Any=None     ,SCREAMING_SNAKE_CASE__			:      Union[str, Any]=None     ,SCREAMING_SNAKE_CASE__			:      Dict=None     ,**SCREAMING_SNAKE_CASE__			:      Optional[int]     ,):
     SCREAMING_SNAKE_CASE:Tuple						      =					{}
     if truncation is not None:
       SCREAMING_SNAKE_CASE:Dict						      =					truncation

     SCREAMING_SNAKE_CASE:Optional[int]						      =					generate_kwargs

     SCREAMING_SNAKE_CASE:Dict						      =					{}
     if return_tensors is not None and return_type is None:
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					ReturnType.TENSORS if return_tensors else ReturnType.TEXT
     if return_type is not None:
       SCREAMING_SNAKE_CASE:List[str]						      =					return_type

     if clean_up_tokenization_spaces is not None:
       SCREAMING_SNAKE_CASE:Any						      =					clean_up_tokenization_spaces

     if stop_sequence is not None:
       SCREAMING_SNAKE_CASE:str						      =					self.tokenizer.encode(SCREAMING_SNAKE_CASE__     ,add_special_tokens=SCREAMING_SNAKE_CASE__							)
       if len(SCREAMING_SNAKE_CASE__							) > 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."							)
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					stop_sequence_ids[0]

     return preprocess_params, forward_params, postprocess_params
   def     __UpperCamelCase						( self			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      int							):
     return True
   def     __UpperCamelCase						( self			:      List[str]     ,*SCREAMING_SNAKE_CASE__			:      Union[str, Any]     ,SCREAMING_SNAKE_CASE__			:      str							):
     SCREAMING_SNAKE_CASE:int						      =					self.model.config.prefix if self.model.config.prefix is not None else ""
     if isinstance(args[0]     ,SCREAMING_SNAKE_CASE__							):
       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"							)
       SCREAMING_SNAKE_CASE:int						      =					([prefix + arg for arg in args[0]],)
       SCREAMING_SNAKE_CASE:List[Any]						      =					True

     elif isinstance(args[0]     ,SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Tuple						      =					(prefix + args[0],)
       SCREAMING_SNAKE_CASE: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`'''							)
     SCREAMING_SNAKE_CASE:str						      =					self.tokenizer(*SCREAMING_SNAKE_CASE__     ,padding=SCREAMING_SNAKE_CASE__     ,truncation=SCREAMING_SNAKE_CASE__     ,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			:      Optional[int]     ,*SCREAMING_SNAKE_CASE__			:      Dict     ,**SCREAMING_SNAKE_CASE__			:      Dict							):
     SCREAMING_SNAKE_CASE:str						      =					super().__call__(*SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
     if (
         isinstance(args[0]     ,SCREAMING_SNAKE_CASE__							)
         and all(isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							) for el in args[0]							)
         and all(len(SCREAMING_SNAKE_CASE__							) == 1 for res in result							)
     ):
       return [res[0] for res in result]
     return result
   def     __UpperCamelCase						( self			:      int     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Any=TruncationStrategy.DO_NOT_TRUNCATE     ,**SCREAMING_SNAKE_CASE__			:      Any							):
     SCREAMING_SNAKE_CASE:List[str]						      =					self._parse_and_tokenize(SCREAMING_SNAKE_CASE__     ,truncation=SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)
     return inputs
   def     __UpperCamelCase						( self			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      str     ,**SCREAMING_SNAKE_CASE__			:      Optional[int]							):
     if self.framework == "pt":
       SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					model_inputs["input_ids"].shape
     elif self.framework == "tf":
       SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					tf.shape(model_inputs["input_ids"]							).numpy()

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







   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Any     ,SCREAMING_SNAKE_CASE__			:      int=ReturnType.TEXT     ,SCREAMING_SNAKE_CASE__			:      str=False							):
     SCREAMING_SNAKE_CASE:Tuple						      =					[]
     for output_ids in model_outputs["output_ids"][0]:
       if return_type == ReturnType.TENSORS:
         SCREAMING_SNAKE_CASE:Dict						      =					{F'''{self.return_name}_token_ids''': output_ids}
       elif return_type == ReturnType.TEXT:
         SCREAMING_SNAKE_CASE:Dict						      =					{
             F'''{self.return_name}_text''': self.tokenizer.decode(
                 SCREAMING_SNAKE_CASE__     ,skip_special_tokens=SCREAMING_SNAKE_CASE__     ,clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__     ,)
         }
       records.append(SCREAMING_SNAKE_CASE__							)
     return records





@add_end_docstrings(_a						)
class 							_snake_case (       _a						):
   _A       :     Optional[int]       =					'''summary'''
   def __call__( self			:      List[Any]     ,*SCREAMING_SNAKE_CASE__			:      str     ,**SCREAMING_SNAKE_CASE__			:      Optional[int]							):
     return super().__call__(*SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)







   def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      int							):
     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 							_snake_case (       _a						):
   _A       :     Dict       =					'''translation'''
   def     __UpperCamelCase						( self			:      str     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      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     __UpperCamelCase						( self			:      List[Any]     ,*SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      List[str]=TruncationStrategy.DO_NOT_TRUNCATE     ,SCREAMING_SNAKE_CASE__			:      List[Any]=None     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=None							):
     if getattr(self.tokenizer     ,"_build_translation_inputs"     ,SCREAMING_SNAKE_CASE__							):
       return self.tokenizer._build_translation_inputs(
           *SCREAMING_SNAKE_CASE__     ,return_tensors=self.framework     ,truncation=SCREAMING_SNAKE_CASE__     ,src_lang=SCREAMING_SNAKE_CASE__     ,tgt_lang=SCREAMING_SNAKE_CASE__							)
     else:
       return super()._parse_and_tokenize(*SCREAMING_SNAKE_CASE__     ,truncation=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      str=None     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=None     ,**SCREAMING_SNAKE_CASE__			:      List[Any]							):
     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					super()._sanitize_parameters(**SCREAMING_SNAKE_CASE__							)
     if src_lang is not None:
       SCREAMING_SNAKE_CASE:List[Any]						      =					src_lang
     if tgt_lang is not None:
       SCREAMING_SNAKE_CASE:str						      =					tgt_lang
     if src_lang is None and tgt_lang is None:
       # Backward compatibility, direct arguments use is preferred.
       SCREAMING_SNAKE_CASE:Any						      =					kwargs.get("task"     ,self.task							)
       SCREAMING_SNAKE_CASE:Dict						      =					task.split("_"							)
       if task and len(SCREAMING_SNAKE_CASE__							) == 4:
         # translation, XX, to YY
         SCREAMING_SNAKE_CASE:Tuple						      =					items[1]
         SCREAMING_SNAKE_CASE:Tuple						      =					items[3]
     return preprocess_params, forward_params, postprocess_params







   def __call__( self			:      Any     ,*SCREAMING_SNAKE_CASE__			:      int     ,**SCREAMING_SNAKE_CASE__			:      Tuple							):
     return super().__call__(*SCREAMING_SNAKE_CASE__     ,**SCREAMING_SNAKE_CASE__							)









'''simple docstring'''

import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
A_       						=							[
    "kernels/rwkv/wkv_cuda.cu",
    "kernels/rwkv/wkv_op.cpp",
    "kernels/deformable_detr/ms_deform_attn.h",
    "kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh",
    "models/graphormer/algos_graphormer.pyx",
]





def    A_  ( snake_case ):
  # Test all the extensions added in the setup
  for file in FILES_TO_FIND:
    if not (transformers_path / file).exists():
      return False
  return True


if __name__ == "__main__":
    A_       						=							argparse.ArgumentParser()
    parser.add_argument("--check_lib", action="store_true", help="Whether to check the build or the actual package.")
    A_       						=							parser.parse_args()
    if args.check_lib:
        A_       						=							importlib.import_module("transformers")
        A_       						=							Path(transformers_module.__file__).parent
    else:
        A_       						=							Path.cwd() / "build/lib/transformers"
    if not test_custom_files_are_present(transformers_path):
        raise ValueError("The built release does not contain the custom files. Fix this before going further!")






