



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      _SCREAMING_SNAKE_CASE		(			unittest.TestCase					):







 '''simple docstring'''

 def __init__(self       :     int ,				UpperCAmelCase_       :     int ,				UpperCAmelCase_       :     Dict=7 ,				UpperCAmelCase_       :     List[Any]=3 ,				UpperCAmelCase_       :     int=18 ,				UpperCAmelCase_       :     Tuple=30 ,				UpperCAmelCase_       :     Union[str, Any]=400 ,				UpperCAmelCase_       :     List[str]=True ,				UpperCAmelCase_       :     Any=None ,				UpperCAmelCase_       :     Optional[Any]=True ,				UpperCAmelCase_       :     Union[str, Any]=None ,				UpperCAmelCase_       :     str=True ,				UpperCAmelCase_       :     str=[0.5, 0.5, 0.5] ,				UpperCAmelCase_       :     str=[0.5, 0.5, 0.5] ,				UpperCAmelCase_       :     Optional[Any]=False ,				) ->Any:



   '''simple docstring'''



   lowerCamelCase__:   Tuple    						=size if size is not None else {"height": 20, "width": 20}
   lowerCamelCase__:   List[Any]    						=crop_size if crop_size is not None else {"height": 18, "width": 18}
   lowerCamelCase__:   List[Any]    						=parent
   lowerCamelCase__:   Optional[int]    						=batch_size
   lowerCamelCase__:   Tuple    						=num_channels
   lowerCamelCase__:   List[Any]    						=image_size
   lowerCamelCase__:   Tuple    						=min_resolution
   lowerCamelCase__:   Any    						=max_resolution
   lowerCamelCase__:   str    						=do_resize
   lowerCamelCase__:   Optional[Any]    						=size
   lowerCamelCase__:   int    						=do_center_crop
   lowerCamelCase__:   Union[str, Any]    						=crop_size
   lowerCamelCase__:   List[str]    						=do_normalize
   lowerCamelCase__:   List[Any]    						=image_mean
   lowerCamelCase__:   int    						=image_std
   lowerCamelCase__:   Any    						=do_reduce_labels







 def 							SCREAMING_SNAKE_CASE_      (self       :     List[str]) ->Union[str, 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   lowerCAmelCase_				( )  ->	int:







  """simple docstring"""





  lowerCamelCase__:   Union[str, Any]    						=load_dataset("hf-internal-testing/fixtures_ade20k"			,					split="test"	)

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

  return image, map





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







  """simple docstring"""





  lowerCamelCase__:   Optional[Any]    						=load_dataset("hf-internal-testing/fixtures_ade20k"			,					split="test"	)

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

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






@require_torch
@require_vision
class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					,							unittest.TestCase					):







 '''simple docstring'''
 lowercase_	=    BeitImageProcessor if is_vision_available() else None

 def 							SCREAMING_SNAKE_CASE_      (self       :     int) ->Dict:



   '''simple docstring'''



   lowerCamelCase__:   Optional[int]    						=BeitImageProcessingTester(self)

 @property
 def 							SCREAMING_SNAKE_CASE_      (self       :     Tuple) ->Optional[Any]:



   '''simple docstring'''



   return self.image_processor_tester.prepare_image_processor_dict()

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



   '''simple docstring'''



   lowerCamelCase__:   Any    						=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 							SCREAMING_SNAKE_CASE_      (self       :     List[str]) ->List[Any]:



   '''simple docstring'''



   lowerCamelCase__:   int    						=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 ,				UpperCAmelCase_)

   lowerCamelCase__:   Optional[Any]    						=self.image_processing_class.from_dict(
       self.image_processor_dict ,				size=42 ,				crop_size=84 ,				reduce_labels=UpperCAmelCase_)
   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 ,				UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_      (self       :     int) ->Optional[int]:



   '''simple docstring'''



   pass

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



   '''simple docstring'''



   lowerCamelCase__:   Optional[Any]    						=self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowerCamelCase__:   Tuple    						=prepare_image_inputs(self.image_processor_tester ,				equal_resolution=UpperCAmelCase_)
   for image in image_inputs:
     self.assertIsInstance(UpperCAmelCase_ ,				Image.Image)

   # Test not batched input
   lowerCamelCase__:   str    						=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
   lowerCamelCase__:   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 							SCREAMING_SNAKE_CASE_      (self       :     Dict) ->List[str]:



   '''simple docstring'''



   lowerCamelCase__:   Union[str, Any]    						=self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowerCamelCase__:   Tuple    						=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
   lowerCamelCase__:   Union[str, 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
   lowerCamelCase__:   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 							SCREAMING_SNAKE_CASE_      (self       :     List[Any]) ->Dict:



   '''simple docstring'''



   lowerCamelCase__:   Tuple    						=self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowerCamelCase__:   Dict    						=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
   lowerCamelCase__:   str    						=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
   lowerCamelCase__:   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 							SCREAMING_SNAKE_CASE_      (self       :     List[Any]) ->Dict:



   '''simple docstring'''



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

   # Test not batched input
   lowerCamelCase__:   List[str]    						=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
   lowerCamelCase__:   Optional[int]    						=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() <= 255)

   # Test not batched input (PIL images)
   lowerCamelCase__:   List[Any]    						=prepare_semantic_single_inputs()

   lowerCamelCase__:   Tuple    						=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() <= 255)

   # Test batched input (PIL images)
   lowerCamelCase__:   Optional[int]    						=prepare_semantic_batch_inputs()

   lowerCamelCase__:   Optional[Any]    						=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() <= 255)







 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[Any]) ->str:



   '''simple docstring'''



   lowerCamelCase__:   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
   lowerCamelCase__:   Optional[Any]    						=prepare_semantic_single_inputs()
   lowerCamelCase__:   Dict    						=image_processing(UpperCAmelCase_ ,				UpperCAmelCase_ ,				return_tensors="pt")
   self.assertTrue(encoding["labels"].min().item() >= 0)
   self.assertTrue(encoding["labels"].max().item() <= 150)

   lowerCamelCase__:   Dict    						=True
   lowerCamelCase__:   List[str]    						=image_processing(UpperCAmelCase_ ,				UpperCAmelCase_ ,				return_tensors="pt")
   self.assertTrue(encoding["labels"].min().item() >= 0)
   self.assertTrue(encoding["labels"].max().item() <= 255)








from __future__ import annotations





def   lowerCAmelCase_				( __a			,					__a			,					__a			,					)  ->	tuple:







  """simple docstring"""





  if (electron_conc, hole_conc, intrinsic_conc).count(0	) != 1:
    raise ValueError("You cannot supply more or less than 2 values"	)
  elif electron_conc < 0:
    raise ValueError("Electron concentration cannot be negative in a semiconductor"	)
  elif hole_conc < 0:
    raise ValueError("Hole concentration cannot be negative in a semiconductor"	)
  elif intrinsic_conc < 0:
    raise ValueError(
        "Intrinsic concentration cannot be negative in a semiconductor"	)
  elif electron_conc == 0:
    return (
        "electron_conc",
        intrinsic_conc**2 / hole_conc,
    )
  elif hole_conc == 0:
    return (
        "hole_conc",
        intrinsic_conc**2 / electron_conc,
    )
  elif intrinsic_conc == 0:
    return (
        "intrinsic_conc",
        (electron_conc * hole_conc) ** 0.5,
    )
  else:
    return (-1, -1)


if __name__ == "__main__":
     import doctest

     doctest.testmod()

