
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 SwinaSRImageProcessor
       from transformers.image_transforms import get_image_size





class 						__lowercase(     unittest.TestCase   ):


       """simple docstring"""







       def __init__(     self					:			Tuple  ,      _lowerCAmelCase					:			int  ,      _lowerCAmelCase					:			Optional[int]=7  ,      _lowerCAmelCase					:			Union[str, Any]=3  ,      _lowerCAmelCase					:			List[Any]=18  ,      _lowerCAmelCase					:			Optional[int]=30  ,      _lowerCAmelCase					:			Any=400  ,      _lowerCAmelCase					:			int=True  ,      _lowerCAmelCase					:			Tuple=1 / 255  ,      _lowerCAmelCase					:			Any=True  ,      _lowerCAmelCase					:			List[Any]=8  ,      )					->   Dict:
         _lowerCAmelCase						=		parent
         _lowerCAmelCase						=		batch_size
         _lowerCAmelCase						=		num_channels
         _lowerCAmelCase						=		image_size
         _lowerCAmelCase						=		min_resolution
         _lowerCAmelCase						=		max_resolution
         _lowerCAmelCase						=		do_rescale
         _lowerCAmelCase						=		rescale_factor
         _lowerCAmelCase						=		do_pad
         _lowerCAmelCase						=		pad_size



       def SCREAMING_SNAKE_CASE_				(     self					:			List[str]       )					->   str:
         return {
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
             "pad_size": self.pad_size,
         }






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

         if equal_resolution:
           _lowerCAmelCase						=		[]
           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:
           _lowerCAmelCase						=		[]
           for i in range(self.batch_size       ):
             _lowerCAmelCase	,       _lowerCAmelCase						=		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
           _lowerCAmelCase						=		[Image.fromarray(np.moveaxis(_lowerCAmelCase  ,      0  ,      -1       )       ) for x in image_inputs]

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

         return image_inputs





@require_torch
@require_vision
class 						__lowercase(     SCREAMING_SNAKE_CASE		,  unittest.TestCase   ):


       """simple docstring"""







       UpperCamelCase_				 =							SwinaSRImageProcessor if is_vision_available() else None



       def SCREAMING_SNAKE_CASE_				(     self					:			Any       )					->   Tuple:
         _lowerCAmelCase						=		SwinaSRImageProcessingTester(self       )



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



       def SCREAMING_SNAKE_CASE_				(     self					:			Optional[int]       )					->   Optional[int]:
         _lowerCAmelCase						=		self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(_lowerCAmelCase  ,      'do_rescale'       )       )
         self.assertTrue(hasattr(_lowerCAmelCase  ,      'rescale_factor'       )       )
         self.assertTrue(hasattr(_lowerCAmelCase  ,      'do_pad'       )       )
         self.assertTrue(hasattr(_lowerCAmelCase  ,      'pad_size'       )       )



       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Union[str, Any]:
         pass



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple  ,      _lowerCAmelCase					:			int       )					->   str:
         _lowerCAmelCase	,       _lowerCAmelCase						=		get_image_size(_lowerCAmelCase       )
         _lowerCAmelCase						=		self.image_processor_tester.pad_size

         _lowerCAmelCase						=		(old_height // size + 1) * size - old_height
         _lowerCAmelCase						=		(old_width // size + 1) * size - old_width
         return old_height + pad_height, old_width + pad_width



       def SCREAMING_SNAKE_CASE_				(     self					:			Tuple       )					->   int:
         # Initialize image_processor
         _lowerCAmelCase						=		self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         _lowerCAmelCase						=		self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase       )
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase  ,      Image.Image       )

         # Test not batched input
         _lowerCAmelCase						=		image_processor(image_inputs[0]  ,      return_tensors='pt'       ).pixel_values
         _lowerCAmelCase	,       _lowerCAmelCase						=		self.calculate_expected_size(np.array(image_inputs[0]       )       )
         self.assertEqual(
             encoded_images.shape  ,      (
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )  ,      )



       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]       )					->   Optional[Any]:
         # Initialize image_processor
         _lowerCAmelCase						=		self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         _lowerCAmelCase						=		self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase  ,      numpify=_lowerCAmelCase       )
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase  ,      np.ndarray       )

         # Test not batched input
         _lowerCAmelCase						=		image_processor(image_inputs[0]  ,      return_tensors='pt'       ).pixel_values
         _lowerCAmelCase	,       _lowerCAmelCase						=		self.calculate_expected_size(image_inputs[0]       )
         self.assertEqual(
             encoded_images.shape  ,      (
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )  ,      )






       def SCREAMING_SNAKE_CASE_				(     self					:			Union[str, Any]       )					->   Optional[Any]:
         # Initialize image_processor
         _lowerCAmelCase						=		self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         _lowerCAmelCase						=		self.image_processor_tester.prepare_inputs(equal_resolution=_lowerCAmelCase  ,      torchify=_lowerCAmelCase       )
         for image in image_inputs:
           self.assertIsInstance(_lowerCAmelCase  ,      torch.Tensor       )

         # Test not batched input
         _lowerCAmelCase						=		image_processor(image_inputs[0]  ,      return_tensors='pt'       ).pixel_values
         _lowerCAmelCase	,       _lowerCAmelCase						=		self.calculate_expected_size(image_inputs[0]       )
         self.assertEqual(
             encoded_images.shape  ,      (
                 1,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )  ,      )


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

from ..features import Features


_UpperCamelCase:					List[Any]     =TypeVar('T', bound='TaskTemplate')





@dataclass(frozen=SCREAMING_SNAKE_CASE   )
class 						__lowercase(     abc.ABC   ):


       """simple docstring"""







       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							42



       def SCREAMING_SNAKE_CASE_				(     self					:			T  ,      _lowerCAmelCase					:			Features       )					->   T:
         return copy.deepcopy(self       )



       @property
       def SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   Features:
         return Features(**self.input_schema  ,      **self.label_schema       )



       @property
       @abc.abstractmethod
       def SCREAMING_SNAKE_CASE_				(     self					:			Dict       )					->   Dict[str, str]:
         raise NotImplementedError






       @classmethod
       def SCREAMING_SNAKE_CASE_				(     cls					:			Type[T]  ,      _lowerCAmelCase					:			dict       )					->   T:
         _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}       )
