"""simple docstring"""






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			:				List[Any]   , _snake_case			:				Optional[Any]   , _snake_case			:				Dict=7   , _snake_case			:				List[str]=3   , _snake_case			:				List[str]=18   , _snake_case			:				List[str]=30   , _snake_case			:				str=400   , _snake_case			:				str=True   , _snake_case			:				int=None   , _snake_case			:				Dict=True   , _snake_case			:				Union[str, Any]=None   , _snake_case			:				Union[str, Any]=True   , _snake_case			:				int=[0.5, 0.5, 0.5]   , _snake_case			:				int=[0.5, 0.5, 0.5]   , _snake_case			:				Optional[int]=False   , )							->       Any:
									SCREAMING_SNAKE_CASE__		  =				size if size is not None else {"height": 20, "width": 20}
									SCREAMING_SNAKE_CASE__		  =				crop_size if crop_size is not None else {"height": 18, "width": 18}
									SCREAMING_SNAKE_CASE__		  =				parent
									SCREAMING_SNAKE_CASE__		  =				batch_size
									SCREAMING_SNAKE_CASE__		  =				num_channels
									SCREAMING_SNAKE_CASE__		  =				image_size
									SCREAMING_SNAKE_CASE__		  =				min_resolution
									SCREAMING_SNAKE_CASE__		  =				max_resolution
									SCREAMING_SNAKE_CASE__		  =				do_resize
									SCREAMING_SNAKE_CASE__		  =				size
									SCREAMING_SNAKE_CASE__		  =				do_center_crop
									SCREAMING_SNAKE_CASE__		  =				crop_size
									SCREAMING_SNAKE_CASE__		  =				do_normalize
									SCREAMING_SNAKE_CASE__		  =				image_mean
									SCREAMING_SNAKE_CASE__		  =				image_std
									SCREAMING_SNAKE_CASE__		  =				do_reduce_labels



					def 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       str:
									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 						SCREAMING_SNAKE_CASE					(  )							->   int:
				SCREAMING_SNAKE_CASE__		  =				load_dataset("hf-internal-testing/fixtures_ade20k"  ,					split="test"       )

				SCREAMING_SNAKE_CASE__		  =				Image.open(dataset[0]["file"]       )
				SCREAMING_SNAKE_CASE__		  =				Image.open(dataset[1]["file"]       )

				return image, map




def 						SCREAMING_SNAKE_CASE					(  )							->   int:
				SCREAMING_SNAKE_CASE__		  =				load_dataset("hf-internal-testing/fixtures_ade20k"  ,					split="test"       )

				SCREAMING_SNAKE_CASE__		  =				Image.open(ds[0]["file"]       )
				SCREAMING_SNAKE_CASE__		  =				Image.open(ds[1]["file"]       )
				SCREAMING_SNAKE_CASE__		  =				Image.open(ds[2]["file"]       )
				SCREAMING_SNAKE_CASE__		  =				Image.open(ds[3]["file"]       )

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

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






					'''simple docstring'''


					a   	=		BeitImageProcessor if is_vision_available() else None



					def 			lowerCAmelCase_					(						self			:				int		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				BeitImageProcessingTester(self		)



					@property
					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Union[str, Any]:
									return self.image_processor_tester.prepare_image_processor_dict()



					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				self.image_processing_class(**self.image_processor_dict		)
									self.assertTrue(hasattr(_snake_case   , "do_resize"		)		)
									self.assertTrue(hasattr(_snake_case   , "size"		)		)
									self.assertTrue(hasattr(_snake_case   , "do_center_crop"		)		)
									self.assertTrue(hasattr(_snake_case   , "center_crop"		)		)
									self.assertTrue(hasattr(_snake_case   , "do_normalize"		)		)
									self.assertTrue(hasattr(_snake_case   , "image_mean"		)		)
									self.assertTrue(hasattr(_snake_case   , "image_std"		)		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				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   , _snake_case		)

									SCREAMING_SNAKE_CASE__		  =				self.image_processing_class.from_dict(
									    self.image_processor_dict   , size=42   , crop_size=84   , reduce_labels=_snake_case		)
									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   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				Tuple		)							->       Tuple:
									pass



					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Optional[int]:
									# Initialize image_processing
									SCREAMING_SNAKE_CASE__		  =				self.image_processing_class(**self.image_processor_dict		)
									# create random PIL images
									SCREAMING_SNAKE_CASE__		  =				prepare_image_inputs(self.image_processor_tester   , equal_resolution=_snake_case		)
									for image in image_inputs:
													self.assertIsInstance(_snake_case   , Image.Image		)

									# Test not batched input
									SCREAMING_SNAKE_CASE__		  =				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__		  =				image_processing(_snake_case   , 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 			lowerCAmelCase_					(						self			:				int		)							->       Optional[int]:
									# Initialize image_processing
									SCREAMING_SNAKE_CASE__		  =				self.image_processing_class(**self.image_processor_dict		)
									# create random numpy tensors
									SCREAMING_SNAKE_CASE__		  =				prepare_image_inputs(self.image_processor_tester   , equal_resolution=_snake_case   , numpify=_snake_case		)
									for image in image_inputs:
													self.assertIsInstance(_snake_case   , np.ndarray		)

									# Test not batched input
									SCREAMING_SNAKE_CASE__		  =				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__		  =				image_processing(_snake_case   , 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 			lowerCAmelCase_					(						self			:				List[Any]		)							->       str:
									# Initialize image_processing
									SCREAMING_SNAKE_CASE__		  =				self.image_processing_class(**self.image_processor_dict		)
									# create random PyTorch tensors
									SCREAMING_SNAKE_CASE__		  =				prepare_image_inputs(self.image_processor_tester   , equal_resolution=_snake_case   , torchify=_snake_case		)
									for image in image_inputs:
													self.assertIsInstance(_snake_case   , torch.Tensor		)

									# Test not batched input
									SCREAMING_SNAKE_CASE__		  =				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__		  =				image_processing(_snake_case   , 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 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Optional[int]:
									# Initialize image_processing
									SCREAMING_SNAKE_CASE__		  =				self.image_processing_class(**self.image_processor_dict		)
									# create random PyTorch tensors
									SCREAMING_SNAKE_CASE__		  =				prepare_image_inputs(self.image_processor_tester   , equal_resolution=_snake_case   , torchify=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				[]
									for image in image_inputs:
													self.assertIsInstance(_snake_case   , torch.Tensor		)
													maps.append(torch.zeros(image.shape[-2:]		).long()		)

									# Test not batched input
									SCREAMING_SNAKE_CASE__		  =				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
									SCREAMING_SNAKE_CASE__		  =				image_processing(_snake_case   , _snake_case   , 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)
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				prepare_semantic_single_inputs()

									SCREAMING_SNAKE_CASE__		  =				image_processing(_snake_case   , _snake_case   , 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)
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				prepare_semantic_batch_inputs()

									SCREAMING_SNAKE_CASE__		  =				image_processing(_snake_case   , _snake_case   , 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 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       Union[str, Any]:
									# Initialize image_processing
									SCREAMING_SNAKE_CASE__		  =				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__  ,       SCREAMING_SNAKE_CASE__		  =				prepare_semantic_single_inputs()
									SCREAMING_SNAKE_CASE__		  =				image_processing(_snake_case   , _snake_case   , return_tensors="pt"		)
									self.assertTrue(encoding["labels"].min().item() >= 0		)
									self.assertTrue(encoding["labels"].max().item() <= 150		)

									SCREAMING_SNAKE_CASE__		  =				True
									SCREAMING_SNAKE_CASE__		  =				image_processing(_snake_case   , _snake_case   , return_tensors="pt"		)
									self.assertTrue(encoding["labels"].min().item() >= 0		)
									self.assertTrue(encoding["labels"].max().item() <= 255		)





"""simple docstring"""






import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


_A   					=    HUGGINGFACE_HUB_CACHE


_A   					=    'config.json'
_A   					=    'diffusion_pytorch_model.bin'
_A   					=    'diffusion_flax_model.msgpack'
_A   					=    'model.onnx'
_A   					=    'diffusion_pytorch_model.safetensors'
_A   					=    'weights.pb'
_A   					=    'https://huggingface.co'
_A   					=    default_cache_path
_A   					=    'diffusers_modules'
_A   					=    os.getenv('HF_MODULES_CACHE', os.path.join(hf_cache_home, 'modules'))
_A   					=    ['fp16', 'non-ema']
_A   					=    '.self_attn'




