


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 ConvNextImageProcessor


class 	a__						(    unittest.TestCase     ):

       def __init__( self   :  str   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Optional[Any]=7   ,				lowerCamelCase_   :  Optional[int]=3   ,				lowerCamelCase_   :  List[str]=1_8   ,				lowerCamelCase_   :  Tuple=3_0   ,				lowerCamelCase_   :  Tuple=4_0_0   ,				lowerCamelCase_   :  Dict=True   ,				lowerCamelCase_   :  Union[str, Any]=None   ,				lowerCamelCase_   :  Optional[int]=0.8_7_5   ,				lowerCamelCase_   :  Optional[int]=True   ,				lowerCamelCase_   :  Dict=[0.5, 0.5, 0.5]   ,				lowerCamelCase_   :  List[str]=[0.5, 0.5, 0.5]   ,				):
            a_     :  List[Any]				      =     size if size is not None else {"""shortest_edge""": 2_0}
            a_     :  List[str]				      =     parent
            a_     :  Union[str, Any]				      =     batch_size
            a_     :  List[str]				      =     num_channels
            a_     :  List[str]				      =     image_size
            a_     :  Optional[Any]				      =     min_resolution
            a_     :  Any				      =     max_resolution
            a_     :  List[str]				      =     do_resize
            a_     :  Dict				      =     size
            a_     :  Optional[Any]				      =     crop_pct
            a_     :  List[Any]				      =     do_normalize
            a_     :  Dict				      =     image_mean
            a_     :  List[Any]				      =     image_std

       def     UpperCAmelCase( self   :  Dict     ):
            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "size": self.size,
                "crop_pct": self.crop_pct,
            }


@require_torch
@require_vision
class 	a__						(    lowerCAmelCase_    ,							unittest.TestCase     ):
       lowerCamelCase__:  Tuple				     =  ConvNextImageProcessor if is_vision_available() else None

       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  Tuple				      =     ConvNextImageProcessingTester(self     )

       @property
       def     UpperCAmelCase( self   :  Any     ):
            return self.image_processor_tester.prepare_image_processor_dict()

       def     UpperCAmelCase( self   :  str     ):
            a_     :  Union[str, Any]				      =     self.image_processing_class(**self.image_processor_dict     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""do_resize"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""size"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""crop_pct"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""do_normalize"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""image_mean"""     )     )
            self.assertTrue(hasattr(lowerCamelCase_   ,				"""image_std"""     )     )

       def     UpperCAmelCase( self   :  Optional[int]     ):
            a_     :  Tuple				      =     self.image_processing_class.from_dict(self.image_processor_dict     )
            self.assertEqual(image_processor.size   ,				{"""shortest_edge""": 2_0}     )

            a_     :  str				      =     self.image_processing_class.from_dict(self.image_processor_dict   ,				size=4_2     )
            self.assertEqual(image_processor.size   ,				{"""shortest_edge""": 4_2}     )

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

       def     UpperCAmelCase( self   :  Any     ):
            # Initialize image_processing
            a_     :  Union[str, Any]				      =     self.image_processing_class(**self.image_processor_dict     )
            # create random PIL images
            a_     :  Any				      =     prepare_image_inputs(self.image_processor_tester   ,				equal_resolution=lowerCamelCase_     )
            for image in image_inputs:
                 self.assertIsInstance(lowerCamelCase_   ,				Image.Image     )

            # Test not batched input
            a_     :  Union[str, 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["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )   ,				)

            # Test batched
            a_     :  Optional[Any]				      =     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["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )   ,				)

       def     UpperCAmelCase( self   :  Tuple     ):
            # Initialize image_processing
            a_     :  Optional[int]				      =     self.image_processing_class(**self.image_processor_dict     )
            # create random numpy tensors
            a_     :  int				      =     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
            a_     :  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["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )   ,				)

            # Test batched
            a_     :  Tuple				      =     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["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )   ,				)

       def     UpperCAmelCase( self   :  str     ):
            # Initialize image_processing
            a_     :  str				      =     self.image_processing_class(**self.image_processor_dict     )
            # create random PyTorch tensors
            a_     :  Optional[int]				      =     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
            a_     :  Dict				      =     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["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )   ,				)

            # Test batched
            a_     :  str				      =     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["""shortest_edge"""],
                    self.image_processor_tester.size["""shortest_edge"""],
                )   ,				)








'''simple docstring'''






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

from ..features import Features


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

@dataclass(frozen=__A       )
class 	_lowerCAmelCase     (    abc.ABC       ):






		'''simple docstring'''
		# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
		snake_case_							= 42
		snake_case_							= 42
		snake_case_							= 42
		def 						__lowercase      (						self							:       T      ,		UpperCamelCase_							:       Features		)      ->			T:







									'''simple docstring'''





									return copy.deepcopy(self		)
		@property
		def 						__lowercase      (						self							:       int		)      ->			Features:







									'''simple docstring'''





									return Features(**self.input_schema      ,		**self.label_schema		)
		@property
		@abc.abstractmethod
		def 						__lowercase      (						self							:       List[str]		)      ->			Dict[str, str]:







									'''simple docstring'''





									raise NotImplementedError





		@classmethod
		def 						__lowercase      (						cls							:       Type[T]      ,		UpperCamelCase_							:       dict		)      ->			T:







									'''simple docstring'''





									_lowercase							:		List[str]				=   {f.name for f in dataclasses.fields(cls		)}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}		)



