

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 LevitImageProcessor






class        A_		(       unittest.TestCase				):
							def __init__(		self		:			int   ,SCREAMING_SNAKE_CASE__		:			Dict   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=7   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=3   ,SCREAMING_SNAKE_CASE__		:			str=1_8   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=3_0   ,SCREAMING_SNAKE_CASE__		:			List[str]=4_0_0   ,SCREAMING_SNAKE_CASE__		:			Any=True   ,SCREAMING_SNAKE_CASE__		:			int=None   ,SCREAMING_SNAKE_CASE__		:			Tuple=True   ,SCREAMING_SNAKE_CASE__		:			str=None   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=True   ,SCREAMING_SNAKE_CASE__		:			List[str]=[0.5, 0.5, 0.5]   ,SCREAMING_SNAKE_CASE__		:			List[Any]=[0.5, 0.5, 0.5]   ,):
								__lowerCamelCase				:				Any					       = size if size is not None else {'shortest_edge': 1_8}
								__lowerCamelCase				:				int					       = crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
								__lowerCamelCase				:				Optional[Any]					       = parent
								__lowerCamelCase				:				Optional[Any]					       = batch_size
								__lowerCamelCase				:				List[Any]					       = num_channels
								__lowerCamelCase				:				List[Any]					       = image_size
								__lowerCamelCase				:				List[Any]					       = min_resolution
								__lowerCamelCase				:				Any					       = max_resolution
								__lowerCamelCase				:				Optional[int]					       = do_resize
								__lowerCamelCase				:				Optional[int]					       = size
								__lowerCamelCase				:				List[Any]					       = do_center_crop
								__lowerCamelCase				:				Tuple					       = crop_size
								__lowerCamelCase				:				List[str]					       = do_normalize
								__lowerCamelCase				:				Optional[Any]					       = image_mean
								__lowerCamelCase				:				int					       = image_std


							def     lowerCAmelCase     (		self		:			List[str]):
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "do_center_crop": self.do_center_crop,
								    "size": self.size,
								    "crop_size": self.crop_size,
								}






@require_torch
@require_vision
class        A_		(       SCREAMING_SNAKE_CASE			,							unittest.TestCase				):
							_UpperCAmelCase    :  List[Any]						      =      LevitImageProcessor if is_vision_available() else None
							def     lowerCAmelCase     (		self		:			int):
								__lowerCamelCase				:				Optional[int]					       = LevitImageProcessingTester(self)
							@property
							def     lowerCAmelCase     (		self		:			int):
								return self.image_processor_tester.prepare_image_processor_dict()
							def     lowerCAmelCase     (		self		:			List[str]):
								__lowerCamelCase				:				List[Any]					       = self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,'image_mean'))
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,'image_std'))
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,'do_normalize'))
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,'do_resize'))
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,'do_center_crop'))
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__   ,'size'))
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								__lowerCamelCase				:				List[str]					       = self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size   ,{'shortest_edge': 1_8})
								self.assertEqual(image_processor.crop_size   ,{'height': 1_8, 'width': 1_8})

								__lowerCamelCase				:				Union[str, Any]					       = self.image_processing_class.from_dict(self.image_processor_dict   ,size=4_2   ,crop_size=8_4)
								self.assertEqual(image_processor.size   ,{'shortest_edge': 4_2})
								self.assertEqual(image_processor.crop_size   ,{'height': 8_4, 'width': 8_4})
							def     lowerCAmelCase     (		self		:			Any):
								pass
							def     lowerCAmelCase     (		self		:			List[str]):
								# Initialize image_processing
								__lowerCamelCase				:				Optional[Any]					       = self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								__lowerCamelCase				:				Any					       = prepare_image_inputs(self.image_processor_tester   ,equal_resolution=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,Image.Image)

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

								# Test batched
								__lowerCamelCase				:				Optional[int]					       = image_processing(SCREAMING_SNAKE_CASE__   ,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     lowerCAmelCase     (		self		:			Dict):
								# Initialize image_processing
								__lowerCamelCase				:				Union[str, Any]					       = self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								__lowerCamelCase				:				List[Any]					       = prepare_image_inputs(self.image_processor_tester   ,equal_resolution=SCREAMING_SNAKE_CASE__   ,numpify=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,np.ndarray)

								# Test not batched input
								__lowerCamelCase				:				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
								__lowerCamelCase				:				Optional[int]					       = image_processing(SCREAMING_SNAKE_CASE__   ,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     lowerCAmelCase     (		self		:			str):
								# Initialize image_processing
								__lowerCamelCase				:				str					       = self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								__lowerCamelCase				:				str					       = prepare_image_inputs(self.image_processor_tester   ,equal_resolution=SCREAMING_SNAKE_CASE__   ,torchify=SCREAMING_SNAKE_CASE__)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE__   ,torch.Tensor)

								# Test not batched input
								__lowerCamelCase				:				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
								__lowerCamelCase				:				str					       = image_processing(SCREAMING_SNAKE_CASE__   ,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'],
								    )   ,)








from typing import Dict

from .base import GenericTensor, Pipeline






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       **_SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         if tokenize_kwargs is None:
           A_      :				Optional[int]		     =					{}

         if truncation is not None:
           if "truncation" in tokenize_kwargs:
             raise ValueError(
                 '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''		)
           A_      :				List[str]		     =					truncation

         A_      :				str		     =					tokenize_kwargs

         A_      :				Optional[Any]		     =					{}
         if return_tensors is not None:
           A_      :				Union[str, Any]		     =					return_tensors

         return preprocess_params, {}, postprocess_params



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Dict[str, GenericTensor]:




         '''simple docstring'''





         A_      :				str		     =					self.framework
         A_      :				Any		     =					self.tokenizer(_SCREAMING_SNAKE_CASE				,       return_tensors=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)
         return model_inputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					self.model(**_SCREAMING_SNAKE_CASE		)
         return model_outputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->List[Any]:




         '''simple docstring'''





         if return_tensors:
           return model_outputs[0]
         if self.framework == "pt":
           return model_outputs[0].tolist()
         elif self.framework == "tf":
           return model_outputs[0].numpy().tolist()







       def __call__(      self				,       *_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         return super().__call__(*_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)




