"""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 EfficientNetImageProcessor






class    __UpperCAmelCase						(	unittest.TestCase	):

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase=13	, _lowerCamelCase=3	, _lowerCamelCase=18	, _lowerCamelCase=30	, _lowerCamelCase=400	, _lowerCamelCase=True	, _lowerCamelCase=None	, _lowerCamelCase=True	, _lowerCamelCase=[0.5, 0.5, 0.5]	, _lowerCamelCase=[0.5, 0.5, 0.5]	, ):
										lowerCamelCase__					=size if size is not None else {"height": 18, "width": 18}
										lowerCamelCase__					=parent
										lowerCamelCase__					=batch_size
										lowerCamelCase__					=num_channels
										lowerCamelCase__					=image_size
										lowerCamelCase__					=min_resolution
										lowerCamelCase__					=max_resolution
										lowerCamelCase__					=do_resize
										lowerCamelCase__					=size
										lowerCamelCase__					=do_normalize
										lowerCamelCase__					=image_mean
										lowerCamelCase__					=image_std






				def 		_a	(      self      ):
										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    __UpperCAmelCase						(	__lowerCAmelCase      ,      unittest.TestCase	):
				A__      :   Any  							=       EfficientNetImageProcessor if is_vision_available() else None

				def 		_a	(      self      ):
										lowerCamelCase__					=EfficientNetImageProcessorTester(self      )

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

				def 		_a	(      self      ):
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										self.assertTrue(hasattr(_lowerCamelCase	, "image_mean"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "image_std"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "do_normalize"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "do_resize"      )      )
										self.assertTrue(hasattr(_lowerCamelCase	, "size"      )      )

				def 		_a	(      self      ):
										lowerCamelCase__					=self.image_processing_class.from_dict(self.image_processor_dict      )
										self.assertEqual(image_processor.size	, {"height": 18, "width": 18}      )

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

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

										# Test not batched input
										lowerCamelCase__					=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
										lowerCamelCase__					=image_processing(_lowerCamelCase	, 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 		_a	(      self      ):
										# Initialize image_processing
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										# create random numpy tensors
										lowerCamelCase__					=prepare_image_inputs(self.image_processor_tester	, equal_resolution=_lowerCamelCase	, numpify=_lowerCamelCase      )
										for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase	, np.ndarray      )

										# Test not batched input
										lowerCamelCase__					=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
										lowerCamelCase__					=image_processing(_lowerCamelCase	, 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 		_a	(      self      ):
										# Initialize image_processing
										lowerCamelCase__					=self.image_processing_class(**self.image_processor_dict      )
										# create random PyTorch tensors
										lowerCamelCase__					=prepare_image_inputs(self.image_processor_tester	, equal_resolution=_lowerCamelCase	, torchify=_lowerCamelCase      )
										for image in image_inputs:
																self.assertIsInstance(_lowerCamelCase	, torch.Tensor      )

										# Test not batched input
										lowerCamelCase__					=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
										lowerCamelCase__					=image_processing(_lowerCamelCase	, 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"],
										    )	, )



"""simple docstring"""


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

from ..features import Features


a							=TypeVar('T', bound='TaskTemplate')






@dataclass(frozen=__lowerCAmelCase	)
class    __UpperCAmelCase						(	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 		_a	(      self	, _lowerCamelCase      ):
										return copy.deepcopy(self      )

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

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






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


