'''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


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import CLIPImageProcessor





class        __magic_name__		(       unittest.TestCase):







			def __init__(      self							:    Optional[Any]							,     lowercase_							:    Optional[Any]							,     lowercase_							:    List[str]=7							,     lowercase_							:    str=3							,     lowercase_							:    Tuple=18							,     lowercase_							:    Union[str, Any]=30							,     lowercase_							:    Optional[Any]=400							,     lowercase_							:    Tuple=True							,     lowercase_							:    Optional[int]=None							,     lowercase_							:    List[str]=True							,     lowercase_							:    Dict=None							,     lowercase_							:    Dict=True							,     lowercase_							:    str=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]							,     lowercase_							:    List[str]=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]							,     lowercase_							:    List[Any]=True							,     ):
										lowercase_   :     List[str]									=			size if size is not None else {"""shortest_edge""": 20}
										lowercase_   :     List[Any]									=			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
										lowercase_   :     Optional[int]									=			parent
										lowercase_   :     Any									=			batch_size
										lowercase_   :     Dict									=			num_channels
										lowercase_   :     str									=			image_size
										lowercase_   :     Dict									=			min_resolution
										lowercase_   :     Union[str, Any]									=			max_resolution
										lowercase_   :     List[str]									=			do_resize
										lowercase_   :     List[str]									=			size
										lowercase_   :     Optional[Any]									=			do_center_crop
										lowercase_   :     List[Any]									=			crop_size
										lowercase_   :     List[str]									=			do_normalize
										lowercase_   :     Union[str, Any]									=			image_mean
										lowercase_   :     List[Any]									=			image_std
										lowercase_   :     Tuple									=			do_convert_rgb







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										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_convert_rgb": self.do_convert_rgb,
										}







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict							,     lowercase_							:    str=False							,     lowercase_							:    Optional[Any]=False							,     lowercase_							:    str=False       ):
										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
																	lowercase_   :     Optional[Any]									=			[]
																	for i in range(self.batch_size       ):
																								image_inputs.append(
																								    np.random.randint(
																								        255							,     size=(self.num_channels, self.max_resolution, self.max_resolution)							,     dtype=np.uinta       )       )
										else:
																	lowercase_   :     List[Any]									=			[]
																	for i in range(self.batch_size       ):
																								lowercase_		,		lowercase_   :     Any									=			np.random.choice(np.arange(self.min_resolution							,     self.max_resolution       )							,     2       )
																								image_inputs.append(np.random.randint(255							,     size=(self.num_channels, width, height)							,     dtype=np.uinta       )       )

										if not numpify and not torchify:
																	# PIL expects the channel dimension as last dimension
																	lowercase_   :     Any									=			[Image.fromarray(np.moveaxis(lowercase_							,     0							,     -1       )       ) for x in image_inputs]

										if torchify:
																	lowercase_   :     int									=			[torch.from_numpy(lowercase_       ) for x in image_inputs]

										return image_inputs





@require_torch
@require_vision
class        __magic_name__		(       _UpperCAmelCase,	unittest.TestCase):
			UpperCamelCase__							=			CLIPImageProcessor if is_vision_available() else None







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]       ):
										lowercase_   :     Tuple									=			CLIPImageProcessingTester(self       )







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										return self.image_processor_tester.prepare_image_processor_dict()







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[Any]       ):
										lowercase_   :     Any									=			self.image_processing_class(**self.image_processor_dict       )
										self.assertTrue(hasattr(lowercase_							,     """do_resize"""       )       )
										self.assertTrue(hasattr(lowercase_							,     """size"""       )       )
										self.assertTrue(hasattr(lowercase_							,     """do_center_crop"""       )       )
										self.assertTrue(hasattr(lowercase_							,     """center_crop"""       )       )
										self.assertTrue(hasattr(lowercase_							,     """do_normalize"""       )       )
										self.assertTrue(hasattr(lowercase_							,     """image_mean"""       )       )
										self.assertTrue(hasattr(lowercase_							,     """image_std"""       )       )
										self.assertTrue(hasattr(lowercase_							,     """do_convert_rgb"""       )       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										lowercase_   :     Union[str, Any]									=			self.image_processing_class.from_dict(self.image_processor_dict       )
										self.assertEqual(image_processor.size							,     {"""shortest_edge""": 20}       )
										self.assertEqual(image_processor.crop_size							,     {"""height""": 18, """width""": 18}       )

										lowercase_   :     Dict									=			self.image_processing_class.from_dict(self.image_processor_dict							,     size=42							,     crop_size=84       )
										self.assertEqual(image_processor.size							,     {"""shortest_edge""": 42}       )
										self.assertEqual(image_processor.crop_size							,     {"""height""": 84, """width""": 84}       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Optional[Any]       ):
										pass







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										# Initialize image_processing
										lowercase_   :     Optional[int]									=			self.image_processing_class(**self.image_processor_dict       )
										# create random PIL images
										lowercase_   :     List[Any]									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_       )
										for image in image_inputs:
																	self.assertIsInstance(lowercase_							,     Image.Image       )

										# Test not batched input
										lowercase_   :     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.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )							,     )

										# Test batched
										lowercase_   :     int									=			image_processing(lowercase_							,     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       ):
										# Initialize image_processing
										lowercase_   :     Optional[int]									=			self.image_processing_class(**self.image_processor_dict       )
										# create random numpy tensors
										lowercase_   :     List[str]									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_							,     numpify=lowercase_       )
										for image in image_inputs:
																	self.assertIsInstance(lowercase_							,     np.ndarray       )

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

										# Test batched
										lowercase_   :     List[str]									=			image_processing(lowercase_							,     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							:    int       ):
										# Initialize image_processing
										lowercase_   :     Dict									=			self.image_processing_class(**self.image_processor_dict       )
										# create random PyTorch tensors
										lowercase_   :     Any									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_							,     torchify=lowercase_       )
										for image in image_inputs:
																	self.assertIsInstance(lowercase_							,     torch.Tensor       )

										# Test not batched input
										lowercase_   :     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.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )							,     )

										# Test batched
										lowercase_   :     Tuple									=			image_processing(lowercase_							,     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"""],
										    )							,     )





@require_torch
@require_vision
class        __magic_name__		(       _UpperCAmelCase,	unittest.TestCase):
			UpperCamelCase__							=			CLIPImageProcessor if is_vision_available() else None







			def       SCREAMING_SNAKE_CASE_	(      self							:    List[str]       ):
										lowercase_   :     Any									=			CLIPImageProcessingTester(self							,     num_channels=4       )
										lowercase_   :     List[Any]									=			3







			@property
			def       SCREAMING_SNAKE_CASE_	(      self							:    str       ):
										return self.image_processor_tester.prepare_image_processor_dict()







			def       SCREAMING_SNAKE_CASE_	(      self							:    str       ):
										lowercase_   :     Dict									=			self.image_processing_class(**self.image_processor_dict       )
										self.assertTrue(hasattr(lowercase_							,     """do_resize"""       )       )
										self.assertTrue(hasattr(lowercase_							,     """size"""       )       )
										self.assertTrue(hasattr(lowercase_							,     """do_center_crop"""       )       )
										self.assertTrue(hasattr(lowercase_							,     """center_crop"""       )       )
										self.assertTrue(hasattr(lowercase_							,     """do_normalize"""       )       )
										self.assertTrue(hasattr(lowercase_							,     """image_mean"""       )       )
										self.assertTrue(hasattr(lowercase_							,     """image_std"""       )       )
										self.assertTrue(hasattr(lowercase_							,     """do_convert_rgb"""       )       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    Dict       ):
										pass







			def       SCREAMING_SNAKE_CASE_	(      self							:    str       ):
										# Initialize image_processing
										lowercase_   :     Optional[int]									=			self.image_processing_class(**self.image_processor_dict       )
										# create random PIL images
										lowercase_   :     str									=			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_       )
										for image in image_inputs:
																	self.assertIsInstance(lowercase_							,     Image.Image       )

										# Test not batched input
										lowercase_   :     Union[str, Any]									=			image_processing(image_inputs[0]							,     return_tensors="""pt"""       ).pixel_values
										self.assertEqual(
										    encoded_images.shape							,     (
										        1,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )							,     )

										# Test batched
										lowercase_   :     Tuple									=			image_processing(lowercase_							,     return_tensors="""pt"""       ).pixel_values
										self.assertEqual(
										    encoded_images.shape							,     (
										        self.image_processor_tester.batch_size,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )							,     )






'''simple docstring'''




_lowercase		:					List[str]													=		"\n# Diffusers installation\n! pip install diffusers transformers datasets accelerate\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/diffusers.git\n"

_lowercase		:					Union[str, Any]													=		[{"type": "code", "content": INSTALL_CONTENT}]





