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 SegformerImageProcessor






class 		a__						( unittest.TestCase ):

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]    				=   size if size is not None else {"height": 30, "width": 30}
							SCREAMING_SNAKE_CASE_  :		str    				=   parent
							SCREAMING_SNAKE_CASE_  :		Any    				=   batch_size
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   num_channels
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   min_resolution
							SCREAMING_SNAKE_CASE_  :		Any    				=   max_resolution
							SCREAMING_SNAKE_CASE_  :		int    				=   do_resize
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   size
							SCREAMING_SNAKE_CASE_  :		str    				=   do_normalize
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   image_mean
							SCREAMING_SNAKE_CASE_  :		int    				=   image_std
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   do_reduce_labels






					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_reduce_labels": self.do_reduce_labels,
							}







def 				_snake_case     (       ):
		"""simple docstring"""







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

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   Image.open(dataset[0]["file"]     )
		SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   Image.open(dataset[1]["file"]     )

		return image, map




def 				_snake_case     (       ):
		"""simple docstring"""







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

		SCREAMING_SNAKE_CASE_  :		Any    				=   Image.open(dataset[0]["file"]     )
		SCREAMING_SNAKE_CASE_  :		Any    				=   Image.open(dataset[1]["file"]     )
		SCREAMING_SNAKE_CASE_  :		Any    				=   Image.open(dataset[2]["file"]     )
		SCREAMING_SNAKE_CASE_  :		Any    				=   Image.open(dataset[3]["file"]     )

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







@require_torch
@require_vision
class 		a__						( A__   ,		unittest.TestCase ):
					A     	=							SegformerImageProcessor if is_vision_available() else None

					def     __UpperCamelCase       (   self				:	List[str]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple    				=   SegformerImageProcessingTester(self   )

					@property
					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							return self.image_processor_tester.prepare_image_processor_dict()

					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(_A,"do_resize"   )   )
							self.assertTrue(hasattr(_A,"size"   )   )
							self.assertTrue(hasattr(_A,"do_normalize"   )   )
							self.assertTrue(hasattr(_A,"image_mean"   )   )
							self.assertTrue(hasattr(_A,"image_std"   )   )
							self.assertTrue(hasattr(_A,"do_reduce_labels"   )   )

					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict    				=   self.image_processing_class.from_dict(self.image_processor_dict   )
							self.assertEqual(image_processor.size,{"height": 30, "width": 30}   )
							self.assertEqual(image_processor.do_reduce_labels,_A   )

							SCREAMING_SNAKE_CASE_  :		Dict    				=   self.image_processing_class.from_dict(self.image_processor_dict,size=42,reduce_labels=_A   )
							self.assertEqual(image_processor.size,{"height": 42, "width": 42}   )
							self.assertEqual(image_processor.do_reduce_labels,_A   )

					def     __UpperCamelCase       (   self				:	str   ):







							"""simple docstring"""






							pass

					def     __UpperCamelCase       (   self				:	Tuple   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   prepare_image_inputs(self.image_processor_tester,equal_resolution=_A   )
							for image in image_inputs:
									self.assertIsInstance(_A,Image.Image   )

							# Test not batched input
							SCREAMING_SNAKE_CASE_  :		int    				=   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.size["height"],
							        self.image_processor_tester.size["width"],
							    ),)

							# Test batched
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   image_processing(_A,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.size["height"],
							        self.image_processor_tester.size["width"],
							    ),)

					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							SCREAMING_SNAKE_CASE_  :		int    				=   prepare_image_inputs(self.image_processor_tester,equal_resolution=_A,numpify=_A   )
							for image in image_inputs:
									self.assertIsInstance(_A,np.ndarray   )

							# Test not batched input
							SCREAMING_SNAKE_CASE_  :		int    				=   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.size["height"],
							        self.image_processor_tester.size["width"],
							    ),)

							# Test batched
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   image_processing(_A,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.size["height"],
							        self.image_processor_tester.size["width"],
							    ),)

					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   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=_A,torchify=_A   )
							for image in image_inputs:
									self.assertIsInstance(_A,torch.Tensor   )

							# Test not batched input
							SCREAMING_SNAKE_CASE_  :		Optional[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.size["height"],
							        self.image_processor_tester.size["width"],
							    ),)

							# Test batched
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   image_processing(_A,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.size["height"],
							        self.image_processor_tester.size["width"],
							    ),)

					def     __UpperCamelCase       (   self				:	Tuple   ):







							"""simple docstring"""






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

							# Test not batched input
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   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.size["height"],
							        self.image_processor_tester.size["width"],
							    ),)
							self.assertEqual(
							    encoding["labels"].shape,(
							        1,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.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
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   image_processing(_A,_A,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.size["height"],
							        self.image_processor_tester.size["width"],
							    ),)
							self.assertEqual(
							    encoding["labels"].shape,(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.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)
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   prepare_semantic_single_inputs()

							SCREAMING_SNAKE_CASE_  :		str    				=   image_processing(_A,_A,return_tensors="pt"   )
							self.assertEqual(
							    encoding["pixel_values"].shape,(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),)
							self.assertEqual(
							    encoding["labels"].shape,(
							        1,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.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)
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   prepare_semantic_batch_inputs()

							SCREAMING_SNAKE_CASE_  :		Dict    				=   image_processing(_A,_A,return_tensors="pt"   )
							self.assertEqual(
							    encoding["pixel_values"].shape,(
							        2,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),)
							self.assertEqual(
							    encoding["labels"].shape,(
							        2,
							        self.image_processor_tester.size["height"],
							        self.image_processor_tester.size["width"],
							    ),)
							self.assertEqual(encoding["labels"].dtype,torch.long   )
							self.assertTrue(encoding["labels"].min().item() >= 0   )
							self.assertTrue(encoding["labels"].max().item() <= 255   )






					def     __UpperCamelCase       (   self				:	Union[str, Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   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_  :		str    				=   prepare_semantic_single_inputs()
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   image_processing(_A,_A,return_tensors="pt"   )
							self.assertTrue(encoding["labels"].min().item() >= 0   )
							self.assertTrue(encoding["labels"].max().item() <= 150   )

							SCREAMING_SNAKE_CASE_  :		int    				=   True
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   image_processing(_A,_A,return_tensors="pt"   )
							self.assertTrue(encoding["labels"].min().item() >= 0   )
							self.assertTrue(encoding["labels"].max().item() <= 255   )

from ..utils import deprecate
from .attention_processor import (  # noqa: F401
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRALinearLayer,
    LoRAXFormersAttnProcessor,
    SlicedAttnAddedKVProcessor,
    SlicedAttnProcessor,
    XFormersAttnProcessor,
)
from .attention_processor import AttnProcessor as AttnProcessorRename  # noqa: F401


deprecate(
    '''cross_attention''',
    '''0.20.0''',
    '''Importing from cross_attention is deprecated. Please import from diffusers.models.attention_processor instead.''',
    standard_warn=False,
)


__lowerCamelCase :     str          =  AttentionProcessor






class 		a__						( A__ ):

					def __init__(   self				:	Tuple,*_A				:	Union[str, Any],**_A				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	str,*_A				:	Optional[Any],**_A				:	List[str]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	str,*_A				:	Dict,**_A				:	Optional[int]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	List[Any],*_A				:	List[str],**_A				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	Optional[int],*_A				:	Any,**_A				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	Union[str, Any],*_A				:	Union[str, Any],**_A				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	Dict,*_A				:	Dict,**_A				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )







class 		a__						( A__ ):

					def __init__(   self				:	int,*_A				:	Dict,**_A				:	Union[str, Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   F'{self.__class__.__name__} is deprecated and will be removed in `0.20.0`. Please use `from diffusers.models.attention_processor import {"".join(self.__class__.__name__.split("Cross"   )   )} instead.'
							deprecate("cross_attention","0.20.0",_A,standard_warn=_A   )
							super().__init__(*_A,**_A   )
