



"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils
class     lowerCamelCase  (      unittest.TestCase    ):






					'''simple docstring'''





					@patch("""diffusers.utils.hub_utils.get_full_repo_name"""						)
					def        lowerCAmelCase_    (	self:							int						,					snake_case:							Mock						)       ->     None:
											snake_case_			:str						=      """full_repo_name"""
											with TemporaryDirectory() as tmpdir:
																	# Dummy args values
																	snake_case_			:int						=      Mock()
																	snake_case_			:Any						=      tmpdir
																	snake_case_			:int						=      0
																	snake_case_			:Optional[int]						=      """hub_token"""
																	snake_case_			:str						=      """dataset_name"""
																	snake_case_			:List[str]						=      0.0_1
																	snake_case_			:Dict						=      100_000
																	snake_case_			:Optional[Any]						=      10_000
																	snake_case_			:Optional[Any]						=      0.0_1
																	snake_case_			:Dict						=      0.0_2
																	snake_case_			:Dict						=      0.0_3
																	snake_case_			:Union[str, Any]						=      0.0_0_0_5
																	snake_case_			:Optional[Any]						=      0.0_0_0_0_0_1
																	snake_case_			:List[Any]						=      1
																	snake_case_			:Optional[Any]						=      10
																	snake_case_			:Dict						=      0.0_0_1
																	snake_case_			:Any						=      0.1
																	snake_case_			:List[Any]						=      0.2
																	snake_case_			:Any						=      True

																	# Model card mush be rendered and saved
																	diffusers.utils.hub_utils.create_model_card(snake_case						,					model_name="""model_name"""						)
																	self.assertTrue((Path(snake_case						) / """README.md""").is_file()						)





"""simple docstring"""
import unittest

import numpy as np

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 ViTImageProcessor
class     lowerCamelCase  (      unittest.TestCase    ):






					'''simple docstring'''





					def __init__(	self:							int						,					snake_case:							List[str]						,					snake_case:							List[str]=7						,					snake_case:							Union[str, Any]=3						,					snake_case:							List[str]=18						,					snake_case:							Any=30						,					snake_case:							Any=400						,					snake_case:							Any=True						,					snake_case:							List[str]=None						,					snake_case:							Union[str, Any]=True						,					snake_case:							int=[0.5, 0.5, 0.5]						,					snake_case:							Tuple=[0.5, 0.5, 0.5]						,					)       ->     int:
											snake_case_			:Dict						=      size if size is not None else {"""height""": 18, """width""": 18}
											snake_case_			:Optional[Any]						=      parent
											snake_case_			:Tuple						=      batch_size
											snake_case_			:Tuple						=      num_channels
											snake_case_			:Optional[int]						=      image_size
											snake_case_			:Tuple						=      min_resolution
											snake_case_			:str						=      max_resolution
											snake_case_			:Any						=      do_resize
											snake_case_			:Union[str, Any]						=      size
											snake_case_			:Tuple						=      do_normalize
											snake_case_			:int						=      image_mean
											snake_case_			:List[Any]						=      image_std






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     List[Any]:
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											}

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






					'''simple docstring'''





					_A		:      Tuple						=					ViTImageProcessor if is_vision_available() else None






					def        lowerCAmelCase_    (	self:							str						)       ->     Optional[Any]:
											snake_case_			:Dict						=      ViTImageProcessingTester(self						)






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






					def        lowerCAmelCase_    (	self:							str						)       ->     List[str]:
											snake_case_			:List[str]						=      self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(snake_case						,					"""image_mean"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""image_std"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""do_normalize"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""do_resize"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""size"""						)						)






					def        lowerCAmelCase_    (	self:							Dict						)       ->     Union[str, Any]:
											snake_case_			:int						=      self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size						,					{"""height""": 18, """width""": 18}						)

											snake_case_			:int						=      self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						)
											self.assertEqual(image_processor.size						,					{"""height""": 42, """width""": 42}						)






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






					def        lowerCAmelCase_    (	self:							Dict						)       ->     Union[str, Any]:
											# Initialize image_processing
											snake_case_			:Any						=      self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											snake_case_			:str						=      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
											snake_case_			:List[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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)

											# Test batched
											snake_case_			:Dict						=      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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)






					def        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     Tuple:
											# Initialize image_processing
											snake_case_			:int						=      self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											snake_case_			:str						=      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
											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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)

											# Test batched
											snake_case_			:Any						=      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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     Optional[int]:
											# Initialize image_processing
											snake_case_			:Union[str, Any]						=      self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											snake_case_			:List[Any]						=      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
											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
											snake_case_			:List[Any]						=      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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)
