





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    ):






  '''simple docstring'''


  def __init__(      self    ,						__lowerCamelCase    ,						__lowerCamelCase=7    ,						__lowerCamelCase=3    ,						__lowerCamelCase=1_8    ,						__lowerCamelCase=3_0    ,						__lowerCamelCase=4_0_0    ,						__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    ,						)			->     Union[str, Any]:
       _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							size if size is not None else {'''height''': 2_0, '''width''': 2_0}
       _SCREAMING_SNAKE_CASE       :	Any						    =							crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
       _SCREAMING_SNAKE_CASE       :	Any						    =							parent
       _SCREAMING_SNAKE_CASE       :	List[Any]						    =							batch_size
       _SCREAMING_SNAKE_CASE       :	Tuple						    =							num_channels
       _SCREAMING_SNAKE_CASE       :	int						    =							image_size
       _SCREAMING_SNAKE_CASE       :	List[Any]						    =							min_resolution
       _SCREAMING_SNAKE_CASE       :	List[Any]						    =							max_resolution
       _SCREAMING_SNAKE_CASE       :	List[Any]						    =							do_resize
       _SCREAMING_SNAKE_CASE       :	int						    =							size
       _SCREAMING_SNAKE_CASE       :	int						    =							do_center_crop
       _SCREAMING_SNAKE_CASE       :	int						    =							crop_size
       _SCREAMING_SNAKE_CASE       :	Tuple						    =							do_normalize
       _SCREAMING_SNAKE_CASE       :	List[str]						    =							image_mean
       _SCREAMING_SNAKE_CASE       :	Any						    =							image_std
       _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							do_reduce_labels







  def     UpperCamelCase_			(      self							)			->     Any:
       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     lowerCamelCase__			():
     _SCREAMING_SNAKE_CASE       :	int						    =							load_dataset("hf-internal-testing/fixtures_ade20k",       split="test"	)

     _SCREAMING_SNAKE_CASE       :	Any						    =							Image.open(dataset[0]["file"]	)
     _SCREAMING_SNAKE_CASE       :	List[Any]						    =							Image.open(dataset[1]["file"]	)

     return image, map






def     lowerCamelCase__			():
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							load_dataset("hf-internal-testing/fixtures_ade20k",       split="test"	)

     _SCREAMING_SNAKE_CASE       :	List[str]						    =							Image.open(ds[0]["file"]	)
     _SCREAMING_SNAKE_CASE       :	int						    =							Image.open(ds[1]["file"]	)
     _SCREAMING_SNAKE_CASE       :	Any						    =							Image.open(ds[2]["file"]	)
     _SCREAMING_SNAKE_CASE       :	Dict						    =							Image.open(ds[3]["file"]	)

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





@require_torch
@require_vision
class 			lowerCAmelCase__( SCREAMING_SNAKE_CASE_  ,     unittest.TestCase    ):






  '''simple docstring'''


  __snake_case 			=    BeitImageProcessor if is_vision_available() else None




  def     UpperCamelCase_			(      self							)			->     List[str]:
       _SCREAMING_SNAKE_CASE       :	str						    =							BeitImageProcessingTester(self							)




  @property
  def     UpperCamelCase_			(      self							)			->     Optional[Any]:
       return self.image_processor_tester.prepare_image_processor_dict()




  def     UpperCamelCase_			(      self							)			->     Any:
       _SCREAMING_SNAKE_CASE       :	str						    =							self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(UpperCamelCase__    ,						"do_resize"							)							)
       self.assertTrue(hasattr(UpperCamelCase__    ,						"size"							)							)
       self.assertTrue(hasattr(UpperCamelCase__    ,						"do_center_crop"							)							)
       self.assertTrue(hasattr(UpperCamelCase__    ,						"center_crop"							)							)
       self.assertTrue(hasattr(UpperCamelCase__    ,						"do_normalize"							)							)
       self.assertTrue(hasattr(UpperCamelCase__    ,						"image_mean"							)							)
       self.assertTrue(hasattr(UpperCamelCase__    ,						"image_std"							)							)




  def     UpperCamelCase_			(      self							)			->     List[str]:
       _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.image_processing_class.from_dict(self.image_processor_dict							)
       self.assertEqual(image_processor.size    ,						{"height": 2_0, "width": 2_0}							)
       self.assertEqual(image_processor.crop_size    ,						{"height": 1_8, "width": 1_8}							)
       self.assertEqual(image_processor.do_reduce_labels    ,						UpperCamelCase__							)

       _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							self.image_processing_class.from_dict(
           self.image_processor_dict    ,						size=4_2    ,						crop_size=8_4    ,						reduce_labels=UpperCamelCase__							)
       self.assertEqual(image_processor.size    ,						{"height": 4_2, "width": 4_2}							)
       self.assertEqual(image_processor.crop_size    ,						{"height": 8_4, "width": 8_4}							)
       self.assertEqual(image_processor.do_reduce_labels    ,						UpperCamelCase__							)




  def     UpperCamelCase_			(      self							)			->     Any:
       pass




  def     UpperCamelCase_			(      self							)			->     Tuple:
       # Initialize image_processing
       _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       _SCREAMING_SNAKE_CASE       :	Any						    =							prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase__							)
       for image in image_inputs:
            self.assertIsInstance(UpperCamelCase__    ,						Image.Image							)

       # Test not batched input
       _SCREAMING_SNAKE_CASE       :	Any						    =							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
       _SCREAMING_SNAKE_CASE       :	List[Any]						    =							image_processing(UpperCamelCase__    ,						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     UpperCamelCase_			(      self							)			->     List[str]:
       # Initialize image_processing
       _SCREAMING_SNAKE_CASE       :	str						    =							self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       _SCREAMING_SNAKE_CASE       :	Dict						    =							prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase__    ,						numpify=UpperCamelCase__							)
       for image in image_inputs:
            self.assertIsInstance(UpperCamelCase__    ,						np.ndarray							)

       # Test not batched input
       _SCREAMING_SNAKE_CASE       :	List[Any]						    =							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
       _SCREAMING_SNAKE_CASE       :	Dict						    =							image_processing(UpperCamelCase__    ,						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     UpperCamelCase_			(      self							)			->     Dict:
       # Initialize image_processing
       _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase__    ,						torchify=UpperCamelCase__							)
       for image in image_inputs:
            self.assertIsInstance(UpperCamelCase__    ,						torch.Tensor							)

       # Test not batched input
       _SCREAMING_SNAKE_CASE       :	Tuple						    =							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
       _SCREAMING_SNAKE_CASE       :	Tuple						    =							image_processing(UpperCamelCase__    ,						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     UpperCamelCase_			(      self							)			->     Dict:
       # Initialize image_processing
       _SCREAMING_SNAKE_CASE       :	List[str]						    =							self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       _SCREAMING_SNAKE_CASE       :	List[Any]						    =							prepare_image_inputs(self.image_processor_tester    ,						equal_resolution=UpperCamelCase__    ,						torchify=UpperCamelCase__							)
       _SCREAMING_SNAKE_CASE       :	int						    =							[]
       for image in image_inputs:
            self.assertIsInstance(UpperCamelCase__    ,						torch.Tensor							)
            maps.append(torch.zeros(image.shape[-2:]							).long()							)

       # Test not batched input
       _SCREAMING_SNAKE_CASE       :	Tuple						    =							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() <= 2_5_5							)

       # Test batched
       _SCREAMING_SNAKE_CASE       :	Any						    =							image_processing(UpperCamelCase__    ,						UpperCamelCase__    ,						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() <= 2_5_5							)

       # Test not batched input (PIL images)
       _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							prepare_semantic_single_inputs()

       _SCREAMING_SNAKE_CASE       :	Dict						    =							image_processing(UpperCamelCase__    ,						UpperCamelCase__    ,						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() <= 2_5_5							)

       # Test batched input (PIL images)
       _SCREAMING_SNAKE_CASE       :	List[str]						    =							prepare_semantic_batch_inputs()

       _SCREAMING_SNAKE_CASE       :	List[str]						    =							image_processing(UpperCamelCase__    ,						UpperCamelCase__    ,						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() <= 2_5_5							)







  def     UpperCamelCase_			(      self							)			->     Tuple:
       # Initialize image_processing
       _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							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
       _SCREAMING_SNAKE_CASE       :	List[Any]						    =							prepare_semantic_single_inputs()
       _SCREAMING_SNAKE_CASE       :	int						    =							image_processing(UpperCamelCase__    ,						UpperCamelCase__    ,						return_tensors="pt"							)
       self.assertTrue(encoding["labels"].min().item() >= 0							)
       self.assertTrue(encoding["labels"].max().item() <= 1_5_0							)

       _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							True
       _SCREAMING_SNAKE_CASE       :	str						    =							image_processing(UpperCamelCase__    ,						UpperCamelCase__    ,						return_tensors="pt"							)
       self.assertTrue(encoding["labels"].min().item() >= 0							)
       self.assertTrue(encoding["labels"].max().item() <= 2_5_5							)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__      ={
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__      =[
           'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CLIPSegModel',
           'CLIPSegPreTrainedModel',
           'CLIPSegTextModel',
           'CLIPSegVisionModel',
           'CLIPSegForImageSegmentation',
       ]

if TYPE_CHECKING:
       from .configuration_clipseg import (
           CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
           CLIPSegConfig,
           CLIPSegTextConfig,
           CLIPSegVisionConfig,
       )
       from .processing_clipseg import CLIPSegProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_clipseg import (
                  CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CLIPSegForImageSegmentation,
                  CLIPSegModel,
                  CLIPSegPreTrainedModel,
                  CLIPSegTextModel,
                  CLIPSegVisionModel,
              )

else:
       import sys

       UpperCamelCase__      =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)