





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    : Tuple    	=  logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase )
class  __A(						__UpperCAmelCase ):

     def __init__(      self,       *A,       **A      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self,       A=None,       A=None,       A=None,       **A      ):





            """simple docstring"""

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

     def __call__(      self,       A,       A = None,       **A      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self,       A,       A=False,       A=False      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						self.model(**A      )
            return model_outputs



     def 	_UpperCamelCase     (      self,       A,       A=5      ):





            """simple docstring"""

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

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

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













import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
       import torch

       from transformers.generation import (
           MaxLengthCriteria,
           MaxNewTokensCriteria,
           MaxTimeCriteria,
           StoppingCriteriaList,
           validate_stopping_criteria,
       )

@require_torch
class  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						3
            _UpperCamelCase              =						250

            _UpperCamelCase              =						ids_tensor((batch_size, length),       A      )
            _UpperCamelCase              =						torch.ones((batch_size, length),       device=A,       dtype=torch.float      ) / length
            return input_ids, scores

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )

            _UpperCamelCase              =						StoppingCriteriaList(
                [
                    MaxLengthCriteria(max_length=10      ),
                    MaxTimeCriteria(max_time=0.1      ),
                ]      )

            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaxLengthCriteria(max_length=10      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaxNewTokensCriteria(start_length=5,       max_new_tokens=5      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

            _UpperCamelCase              =						StoppingCriteriaList([criteria]      )
            self.assertEqual(criteria_list.max_length,       10      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )

            _UpperCamelCase              =						MaxTimeCriteria(max_time=0.1      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase              =						MaxTimeCriteria(max_time=0.1,       initial_timestamp=time.time() - 0.2      )
            self.assertTrue(criteria(A,       A      )      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      ),       10      )

            with self.assertWarns(A      ):
                   validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      ),       11      )

            _UpperCamelCase              =						validate_stopping_criteria(StoppingCriteriaList(),       11      )

            self.assertEqual(len(A      ),       1      )






