





'''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

_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase__						)
class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,*_lowerCamelCase     ,**_lowerCamelCase )							->							int:







				'''simple docstring'''





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


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,**_lowerCamelCase )							->							Optional[int]:







				'''simple docstring'''





				__lowercase						,		__lowercase										=							{}, {}
				if padding is not None:
							__lowercase										=							padding
				if truncation is not None:
							__lowercase										=							truncation
				if top_k is not None:
							__lowercase										=							top_k
				return preprocess_params, {}, postprocess_params


	def __call__(self     ,_lowerCamelCase     ,_lowerCamelCase = None     ,**_lowerCamelCase )							->							str:







				'''simple docstring'''





				if isinstance(_lowerCamelCase     ,(Image.Image, str) ) and isinstance(_lowerCamelCase     ,_lowerCamelCase ):
							__lowercase										=							{'''image''': image, '''question''': question}
				else:
							__lowercase										=							image
				__lowercase										=							super().__call__(_lowerCamelCase     ,**_lowerCamelCase )
				return results


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase=False     ,_lowerCamelCase=False )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							load_image(inputs['''image'''] )
				__lowercase										=							self.tokenizer(
				    inputs['''question''']     ,return_tensors=self.framework     ,padding=_lowerCamelCase     ,truncation=_lowerCamelCase )
				__lowercase										=							self.image_processor(images=_lowerCamelCase     ,return_tensors=self.framework )
				model_inputs.update(_lowerCamelCase )
				return model_inputs


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							int:







				'''simple docstring'''





				__lowercase										=							self.model(**_lowerCamelCase )
				return model_outputs


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase=5 )							->							Tuple:







				'''simple docstring'''





				if top_k > self.model.config.num_labels:
							__lowercase										=							self.model.config.num_labels

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

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












'''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


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					=    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

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

_SCREAMING_SNAKE_CASE					=    {
    '''gpt-neox-20b''': 2_0_4_8,
}






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	a    :				List[Any]											=       VOCAB_FILES_NAMES
	a    :				Optional[Any]											=       PRETRAINED_VOCAB_FILES_MAP
	a    :				Optional[int]											=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a    :				List[str]											=       ["input_ids", "attention_mask"]


	def __init__(self     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase=None     ,_lowerCamelCase="<|endoftext|>"     ,_lowerCamelCase="<|endoftext|>"     ,_lowerCamelCase="<|endoftext|>"     ,_lowerCamelCase=False     ,**_lowerCamelCase     ,)							->							Optional[Any]:







				'''simple docstring'''





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

				__lowercase										=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
				if pre_tok_state.get('''add_prefix_space'''     ,_lowerCamelCase ) != add_prefix_space:
							__lowercase										=							getattr(_lowerCamelCase     ,pre_tok_state.pop('''type''' ) )
							__lowercase										=							add_prefix_space
							__lowercase										=							pre_tok_class(**_lowerCamelCase )

				__lowercase										=							add_prefix_space


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = None )							->							Tuple[str]:







				'''simple docstring'''





				__lowercase										=							self._tokenizer.model.save(_lowerCamelCase     ,name=_lowerCamelCase )
				return tuple(_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							List[int]:







				'''simple docstring'''





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

				if len(_lowerCamelCase ) > self.model_max_length:
							__lowercase										=							input_ids[-self.model_max_length :]
				return input_ids





