




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

_lowerCamelCase	:		Union[str, Any]							       =   logging.get_logger(__name__)


@add_end_docstrings(UpperCAmelCase__     )
class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    def __init__(			self      :	Optional[Any]					, *UpperCAmelCase__      :	List[str]					, **UpperCAmelCase__      :	Dict)						->Dict:





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	List[str]					, UpperCAmelCase__      :	int=None					, UpperCAmelCase__      :	Optional[int]=None					, UpperCAmelCase__      :	int=None					, **UpperCAmelCase__      :	Any)						->str:





           '''simple docstring'''

           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      :	Dict					, UpperCAmelCase__      :	Union["Image.Image", str]					, UpperCAmelCase__      :	str = None					, **UpperCAmelCase__      :	List[Any])						->List[Any]:





           '''simple docstring'''

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


    def        SCREAMING_SNAKE_CASE							(			self      :	List[Any]					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Union[str, Any]=False					, UpperCAmelCase__      :	Tuple=False)						->Optional[Any]:





           '''simple docstring'''

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


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





           '''simple docstring'''

           A__       =    self.model(**UpperCAmelCase__)
           return model_outputs





    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any]					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Optional[int]=5)						->List[Any]:





           '''simple docstring'''

           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(UpperCAmelCase__)
           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(UpperCAmelCase__					, UpperCAmelCase__)]








import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_lowerCamelCase	:		List[Any]							       =   """sshleifer/bart-tiny-random"""
_lowerCamelCase	:		List[Any]							       =   """patrickvonplaten/t5-tiny-random"""


@require_torch
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    @cached_property
    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->int:





           '''simple docstring'''

           return AutoConfig.from_pretrained(UpperCAmelCase__)


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





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.num_hidden_layers					, 1)


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->Union[str, Any]:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, self.teacher_config.encoder_layers)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->int:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, 1)





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





           '''simple docstring'''

           with self.assertRaises(UpperCAmelCase__):
                  create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=UpperCAmelCase__					, d=UpperCAmelCase__)


