




"""simple docstring"""


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''': 20_48,
}






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					VOCAB_FILES_NAMES
 lowercase__ 					=					PRETRAINED_VOCAB_FILES_MAP
 lowercase__ 					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase__ 					=					["input_ids", "attention_mask"]


 def __init__(				self:		Dict,      a_:		Dict=None,      a_:		Tuple=None,      a_:		Dict=None,      a_:		str="<|endoftext|>",      a_:		Union[str, Any]="<|endoftext|>",      a_:		Tuple="<|endoftext|>",      a_:		Optional[int]=False,      **a_:		str,      ):




   '''simple docstring'''



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

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

   _snake_case							:		Tuple								=  add_prefix_space


 def  UpperCamelCase_   (				self:		List[Any],      a_:		str,      a_:		Optional[str] = None		):




   '''simple docstring'''



   _snake_case							:		Dict								=  self._tokenizer.model.save(a_,      name=a_		)
   return tuple(a_		)


 def  UpperCamelCase_   (				self:		List[Any],      a_:		"Conversation"		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  []
   for is_user, text in conversation.iter_texts():
     input_ids.extend(self.encode(a_,      add_special_tokens=a_		) + [self.eos_token_id]		)

   if len(a_		) > self.model_max_length:
     _snake_case							:		int								=  input_ids[-self.model_max_length :]
   return input_ids




'''simple docstring'''




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

lowercase			:				str    		=			logging.get_logger(__name__)





@add_end_docstrings(__snake_case						)
class  A				(							__snake_case						):





     def __init__(							self						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """simple docstring"""






         super().__init__(*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         self.check_model_type(SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			**SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         A,  A       :   Dict					=						{}, {}
         if padding is not None:
             A       :   List[str]					=						padding
         if truncation is not None:
             A       :   Dict					=						truncation
         if top_k is not None:
             A       :   Optional[Any]					=						top_k
         return preprocess_params, {}, postprocess_params





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE	) ->				Dict:






         """simple docstring"""






         if isinstance(SCREAMING_SNAKE_CASE						,			(Image.Image, str)	) and isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
             A       :   int					=						{'''image''': image, '''question''': question}
         else:
             A       :   Any					=						image
         A       :   Any					=						super().__call__(SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         return results





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE=False	) ->				Any:






         """simple docstring"""






         A       :   Union[str, Any]					=						load_image(inputs['''image''']	)
         A       :   Optional[Any]					=						self.tokenizer(
             inputs['''question''']						,			return_tensors=self.framework						,			padding=SCREAMING_SNAKE_CASE						,			truncation=SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						self.image_processor(images=SCREAMING_SNAKE_CASE						,			return_tensors=self.framework	)
         model_inputs.update(SCREAMING_SNAKE_CASE	)
         return model_inputs





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         A       :   List[Any]					=						self.model(**SCREAMING_SNAKE_CASE	)
         return model_outputs





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=5	) ->				int:






         """simple docstring"""






         if top_k > self.model.config.num_labels:
             A       :   Dict					=						self.model.config.num_labels

         if self.framework == "pt":
             A       :   Optional[int]					=						model_outputs.logits.sigmoid()[0]
             A,  A       :   int					=						probs.topk(SCREAMING_SNAKE_CASE	)
         else:
             raise ValueError(F'Unsupported framework: {self.framework}'	)

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




