





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


__A							   =							logging.get_logger(__name__)

__A							   =							{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__A							   =							{
    "tokenizer_file": {
        "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
    },
}

__A							   =							{
    "gpt-neox-20b": 2048,
}



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     VOCAB_FILES_NAMES
 lowercase_						=     PRETRAINED_VOCAB_FILES_MAP
 lowercase_						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_						=     ["input_ids", "attention_mask"]

 def __init__(self							:  int			,					UpperCAmelCase_							:  int=None			,					UpperCAmelCase_							:  Any=None			,					UpperCAmelCase_							:  Tuple=None			,					UpperCAmelCase_							:  List[str]="<|endoftext|>"			,					UpperCAmelCase_							:  Optional[int]="<|endoftext|>"			,					UpperCAmelCase_							:  int="<|endoftext|>"			,					UpperCAmelCase_							:  int=False			,					**UpperCAmelCase_							:  Optional[int]			,					)			->int:



   '''simple docstring'''



   super().__init__(
       UpperCAmelCase_			,					UpperCAmelCase_			,					tokenizer_file=UpperCAmelCase_			,					unk_token=UpperCAmelCase_			,					bos_token=UpperCAmelCase_			,					eos_token=UpperCAmelCase_			,					add_prefix_space=UpperCAmelCase_			,					**UpperCAmelCase_			,					)

   lowerCamelCase__: Union[str, Any]         =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
   if pre_tok_state.get("add_prefix_space"			,					UpperCAmelCase_) != add_prefix_space:
     lowerCamelCase__: Dict         =getattr(UpperCAmelCase_			,					pre_tok_state.pop("type"))
     lowerCamelCase__: Optional[int]         =add_prefix_space
     lowerCamelCase__: Any         =pre_tok_class(**UpperCAmelCase_)

   lowerCamelCase__: Optional[Any]         =add_prefix_space

 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  Optional[str] = None)			->Tuple[str]:



   '''simple docstring'''



   lowerCamelCase__: str         =self._tokenizer.model.save(UpperCAmelCase_			,					name=UpperCAmelCase_)
   return tuple(UpperCAmelCase_)







 def 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					UpperCAmelCase_							:  "Conversation")			->List[int]:



   '''simple docstring'''



   lowerCamelCase__: int         =[]
   for is_user, text in conversation.iter_texts():
     input_ids.extend(self.encode(UpperCAmelCase_			,					add_special_tokens=UpperCAmelCase_) + [self.eos_token_id])

   if len(UpperCAmelCase_) > self.model_max_length:
     lowerCamelCase__: str         =input_ids[-self.model_max_length :]
   return input_ids






from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

a_      :      str    =     logging.get_logger(__name__)
@add_end_docstrings(_lowercase				)
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 def __init__(	self		,				*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			int:
  super().__init__(*SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
  self.check_model_type(SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__							,			a__												= {}, {}
  if padding is not None:
   a__												= padding
  if truncation is not None:
   a__												= truncation
  if top_k is not None:
   a__												= top_k
  return preprocess_params, {}, postprocess_params

 def __call__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  if isinstance(SCREAMING_SNAKE_CASE		,				(Image.Image, str)      ) and isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
   a__												= {'''image''': image, '''question''': question}
  else:
   a__												= image
  a__												= super().__call__(SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
  return results

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=False		,				SCREAMING_SNAKE_CASE=False      ) ->			Dict:
  a__												= load_image(inputs['''image''']      )
  a__												= self.tokenizer(
      inputs['''question''']		,				return_tensors=self.framework		,				padding=SCREAMING_SNAKE_CASE		,				truncation=SCREAMING_SNAKE_CASE      )
  a__												= self.image_processor(images=SCREAMING_SNAKE_CASE		,				return_tensors=self.framework      )
  model_inputs.update(SCREAMING_SNAKE_CASE      )
  return model_inputs

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__												= self.model(**SCREAMING_SNAKE_CASE      )
  return model_outputs



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=5      ) ->			Optional[Any]:
  if top_k > self.model.config.num_labels:
   a__												= self.model.config.num_labels

  if self.framework == "pt":
   a__												= model_outputs.logits.sigmoid()[0]
   a__							,			a__												= probs.topk(SCREAMING_SNAKE_CASE      )
  else:
   raise ValueError(f"Unsupported framework: {self.framework}"      )

  a__												= scores.tolist()
  a__												= ids.tolist()
  return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )]





