
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 DeiTImageProcessor



class       a_   ( unittest.TestCase						):






			def __init__(			self							, __UpperCamelCase							, __UpperCamelCase=7							, __UpperCamelCase=3							, __UpperCamelCase=18							, __UpperCamelCase=30							, __UpperCamelCase=400							, __UpperCamelCase=True							, __UpperCamelCase=None							, __UpperCamelCase=True							, __UpperCamelCase=None							, __UpperCamelCase=True							, __UpperCamelCase=[0.5, 0.5, 0.5]							, __UpperCamelCase=[0.5, 0.5, 0.5]							, ):
									_lowercase									=				size if size is not None else {"""height""": 20, """width""": 20}
									_lowercase									=				crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

									_lowercase									=				parent
									_lowercase									=				batch_size
									_lowercase									=				num_channels
									_lowercase									=				image_size
									_lowercase									=				min_resolution
									_lowercase									=				max_resolution
									_lowercase									=				do_resize
									_lowercase									=				size
									_lowercase									=				do_center_crop
									_lowercase									=				crop_size
									_lowercase									=				do_normalize
									_lowercase									=				image_mean
									_lowercase									=				image_std






			def       UpperCamelCase_							(			self     ):
									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,
									}







@require_torch
@require_vision
class       a_   ( _a				, unittest.TestCase						):
			a  : List[str]				      =       DeiTImageProcessor if is_vision_available() else None
			a  : Dict				      =       True






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				DeiTImageProcessingTester(self     )






			@property
			def       UpperCamelCase_							(			self     ):
									return self.image_processor_tester.prepare_image_processor_dict()






			def       UpperCamelCase_							(			self     ):
									_lowercase									=				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       UpperCamelCase_							(			self     ):
									_lowercase									=				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}     )

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






			def       UpperCamelCase_							(			self     ):
									pass






			def       UpperCamelCase_							(			self     ):
									# Initialize image_processing
									_lowercase									=				self.image_processing_class(**self.image_processor_dict     )
									# create random PIL images
									_lowercase									=				prepare_image_inputs(self.image_processor_tester							, equal_resolution=__UpperCamelCase     )
									for image in image_inputs:
															self.assertIsInstance(__UpperCamelCase							, Image.Image     )

									# Test not batched input
									_lowercase									=				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									=				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       UpperCamelCase_							(			self     ):
									# Initialize image_processing
									_lowercase									=				self.image_processing_class(**self.image_processor_dict     )
									# create random numpy tensors
									_lowercase									=				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
									_lowercase									=				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									=				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       UpperCamelCase_							(			self     ):
									# Initialize image_processing
									_lowercase									=				self.image_processing_class(**self.image_processor_dict     )
									# create random PyTorch tensors
									_lowercase									=				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
									_lowercase									=				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									=				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"""],
									    )							, )

import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


A		:   Optional[int]         =    TypeVar('''T''', bound='''TaskTemplate''')



@dataclass(frozen=_a						)
class       a_   ( abc.ABC						):
			# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
			a  : str
			a  : ClassVar[Features]
			a  : ClassVar[Features]






			def       UpperCamelCase_							(			self							, __UpperCamelCase     ):
									return copy.deepcopy(self     )






			@property
			def       UpperCamelCase_							(			self     ):
									return Features(**self.input_schema							, **self.label_schema     )






			@property
			@abc.abstractmethod
			def       UpperCamelCase_							(			self     ):
									raise NotImplementedError






			@classmethod
			def       UpperCamelCase_							(			cls							, __UpperCamelCase     ):
									_lowercase									=				{f.name for f in dataclasses.fields(cls     )}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}     )