

from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
  from PIL import Image





class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						"""Salesforce/blip-image-captioning-base"""
  _a		    =						(
      """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
      """image to caption, and returns a text that contains the description in English."""
  )
  _a		    =						"""image_captioner"""
  _a		    =						AutoModelForVisionaSeq

  _a		    =						["""image"""]
  _a		    =						["""text"""]




  def __init__(       self							,     *lowerCAmelCase							,     **lowerCAmelCase      )						->				Tuple:
        '''simple docstring'''






        requires_backends(self							,     ['vision']      )
        super().__init__(*__UpperCamelCase							,     **__UpperCamelCase      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				Optional[Any]:
        '''simple docstring'''






        return self.pre_processor(images=__UpperCamelCase							,     return_tensors='pt'      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				Optional[Any]:
        '''simple docstring'''






        return self.model.generate(**__UpperCamelCase      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				int:
        '''simple docstring'''






        return self.pre_processor.batch_decode(__UpperCamelCase							,     skip_special_tokens=__UpperCamelCase      )[0].strip()





import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BeitImageProcessor





class 				__lowerCAmelCase  (      unittest.TestCase							):




  def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase=7							,     lowerCAmelCase=3							,     lowerCAmelCase=18							,     lowerCAmelCase=30							,     lowerCAmelCase=400							,     lowerCAmelCase=True							,     lowerCAmelCase=None							,     lowerCAmelCase=True							,     lowerCAmelCase=None							,     lowerCAmelCase=True							,     lowerCAmelCase=[0.5, 0.5, 0.5]							,     lowerCAmelCase=[0.5, 0.5, 0.5]							,     lowerCAmelCase=False							,     )						->				Tuple:
        '''simple docstring'''






        _lowercase			 =size if size is not None else {'height': 20, 'width': 20}
        _lowercase			 =crop_size if crop_size is not None else {'height': 18, 'width': 18}
        _lowercase			 =parent
        _lowercase			 =batch_size
        _lowercase			 =num_channels
        _lowercase			 =image_size
        _lowercase			 =min_resolution
        _lowercase			 =max_resolution
        _lowercase			 =do_resize
        _lowercase			 =size
        _lowercase			 =do_center_crop
        _lowercase			 =crop_size
        _lowercase			 =do_normalize
        _lowercase			 =image_mean
        _lowercase			 =image_std
        _lowercase			 =do_reduce_labels




  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_reduce_labels": self.do_reduce_labels,
        }
def   a   (    )					->   Tuple:





      """simple docstring"""



      _lowercase			 =load_dataset('hf-internal-testing/fixtures_ade20k'   ,     split='test'  )

      _lowercase			 =Image.open(dataset[0]['file']  )
      _lowercase			 =Image.open(dataset[1]['file']  )

      return image, map
def   a   (    )					->   Union[str, Any]:





      """simple docstring"""



      _lowercase			 =load_dataset('hf-internal-testing/fixtures_ade20k'   ,     split='test'  )

      _lowercase			 =Image.open(ds[0]['file']  )
      _lowercase			 =Image.open(ds[1]['file']  )
      _lowercase			 =Image.open(ds[2]['file']  )
      _lowercase			 =Image.open(ds[3]['file']  )

      return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):
  _a		    =						BeitImageProcessor if is_vision_available() else None




  def 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        _lowercase			 =BeitImageProcessingTester(self      )




  @property
  def 						A__  (       self      )						->				int:
        '''simple docstring'''






        return self.image_processor_tester.prepare_image_processor_dict()




  def 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'do_resize'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'size'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'do_center_crop'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'center_crop'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'do_normalize'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'image_mean'      )      )
        self.assertTrue(hasattr(lowerCAmelCase							,     'image_std'      )      )




  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class.from_dict(self.image_processor_dict      )
        self.assertEqual(image_processor.size							,     {'height': 20, 'width': 20}      )
        self.assertEqual(image_processor.crop_size							,     {'height': 18, 'width': 18}      )
        self.assertEqual(image_processor.do_reduce_labels							,     lowerCAmelCase      )

        _lowercase			 =self.image_processing_class.from_dict(
            self.image_processor_dict							,     size=42							,     crop_size=84							,     reduce_labels=lowerCAmelCase      )
        self.assertEqual(image_processor.size							,     {'height': 42, 'width': 42}      )
        self.assertEqual(image_processor.crop_size							,     {'height': 84, 'width': 84}      )
        self.assertEqual(image_processor.do_reduce_labels							,     lowerCAmelCase      )




  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        pass




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        # create random PIL images
        _lowercase			 =prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase      )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,     Image.Image      )

        # Test not batched input
        _lowercase			 =image_processing(image_inputs[0]							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )

        # Test batched
        _lowercase			 =image_processing(lowerCAmelCase							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        # create random numpy tensors
        _lowercase			 =prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase							,     numpify=lowerCAmelCase      )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,     np.ndarray      )

        # Test not batched input
        _lowercase			 =image_processing(image_inputs[0]							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )

        # Test batched
        _lowercase			 =image_processing(lowerCAmelCase							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )




  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        # create random PyTorch tensors
        _lowercase			 =prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase							,     torchify=lowerCAmelCase      )
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,     torch.Tensor      )

        # Test not batched input
        _lowercase			 =image_processing(image_inputs[0]							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )

        # Test batched
        _lowercase			 =image_processing(lowerCAmelCase							,     return_tensors='pt'      ).pixel_values
        self.assertEqual(
            encoded_images.shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )
        # create random PyTorch tensors
        _lowercase			 =prepare_image_inputs(self.image_processor_tester							,     equal_resolution=lowerCAmelCase							,     torchify=lowerCAmelCase      )
        _lowercase			 =[]
        for image in image_inputs:
              self.assertIsInstance(lowerCAmelCase							,     torch.Tensor      )
              maps.append(torch.zeros(image.shape[-2:]      ).long()      )

        # Test not batched input
        _lowercase			 =image_processing(image_inputs[0]							,     maps[0]							,     return_tensors='pt'      )
        self.assertEqual(
            encoding['pixel_values'].shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(
            encoding['labels'].shape							,     (
                1,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(encoding['labels'].dtype							,     torch.long      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )

        # Test batched
        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertEqual(
            encoding['pixel_values'].shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(
            encoding['labels'].shape							,     (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(encoding['labels'].dtype							,     torch.long      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )

        # Test not batched input (PIL images)
        _lowercase , _lowercase			 =prepare_semantic_single_inputs()

        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertEqual(
            encoding['pixel_values'].shape							,     (
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(
            encoding['labels'].shape							,     (
                1,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(encoding['labels'].dtype							,     torch.long      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )

        # Test batched input (PIL images)
        _lowercase , _lowercase			 =prepare_semantic_batch_inputs()

        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertEqual(
            encoding['pixel_values'].shape							,     (
                2,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(
            encoding['labels'].shape							,     (
                2,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )							,     )
        self.assertEqual(encoding['labels'].dtype							,     torch.long      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )




  def 						A__  (       self      )						->				List[Any]:
        '''simple docstring'''






        _lowercase			 =self.image_processing_class(**self.image_processor_dict      )

        # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
        _lowercase , _lowercase			 =prepare_semantic_single_inputs()
        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 150      )

        _lowercase			 =True
        _lowercase			 =image_processing(lowerCAmelCase							,     lowerCAmelCase							,     return_tensors='pt'      )
        self.assertTrue(encoding['labels'].min().item() >= 0      )
        self.assertTrue(encoding['labels'].max().item() <= 255      )


