
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 MobileViTImageProcessor


class   __lowerCAmelCase		(		unittest.TestCase					):



      """simple docstring"""







      def __init__(					self						:				Optional[int] ,				_snake_case						:				str ,				_snake_case						:				Optional[Any]=7 ,				_snake_case						:				List[str]=3 ,				_snake_case						:				Optional[Any]=18 ,				_snake_case						:				int=30 ,				_snake_case						:				List[Any]=400 ,				_snake_case						:				str=True ,				_snake_case						:				List[str]=None ,				_snake_case						:				str=True ,				_snake_case						:				Optional[int]=None ,				_snake_case						:				List[Any]=True ,				):
          __lowercase						:	int					=				size if size is not None else {'''shortest_edge''': 20}
          __lowercase						:	int					=				crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
          __lowercase						:	Optional[int]					=				parent
          __lowercase						:	List[str]					=				batch_size
          __lowercase						:	List[str]					=				num_channels
          __lowercase						:	Optional[int]					=				image_size
          __lowercase						:	Tuple					=				min_resolution
          __lowercase						:	Optional[int]					=				max_resolution
          __lowercase						:	Optional[int]					=				do_resize
          __lowercase						:	int					=				size
          __lowercase						:	int					=				do_center_crop
          __lowercase						:	str					=				crop_size
          __lowercase						:	int					=				do_flip_channel_order




      def 						snake_case_	(					self						:				Optional[Any] ):
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
              "do_flip_channel_order": self.do_flip_channel_order,
          }






@require_torch
@require_vision
class   __lowerCAmelCase		(		_UpperCAmelCase       ,       unittest.TestCase					):



      """simple docstring"""







      A__		:	Optional[int]         =			MobileViTImageProcessor if is_vision_available() else None




      def 						snake_case_	(					self						:				Optional[Any] ):
          __lowercase						:	str					=				MobileViTImageProcessingTester(self )




      @property
      def 						snake_case_	(					self						:				int ):
          return self.image_processor_tester.prepare_image_processor_dict()




      def 						snake_case_	(					self						:				Optional[Any] ):
          __lowercase						:	str					=				self.image_processing_class(**self.image_processor_dict )
          self.assertTrue(hasattr(lowercase_ ,				'''do_resize''' ) )
          self.assertTrue(hasattr(lowercase_ ,				'''size''' ) )
          self.assertTrue(hasattr(lowercase_ ,				'''do_center_crop''' ) )
          self.assertTrue(hasattr(lowercase_ ,				'''center_crop''' ) )
          self.assertTrue(hasattr(lowercase_ ,				'''do_flip_channel_order''' ) )




      def 						snake_case_	(					self						:				Any ):
          __lowercase						:	str					=				self.image_processing_class.from_dict(self.image_processor_dict )
          self.assertEqual(image_processor.size ,				{'''shortest_edge''': 20} )
          self.assertEqual(image_processor.crop_size ,				{'''height''': 18, '''width''': 18} )

          __lowercase						:	Union[str, Any]					=				self.image_processing_class.from_dict(self.image_processor_dict ,				size=42 ,				crop_size=84 )
          self.assertEqual(image_processor.size ,				{'''shortest_edge''': 42} )
          self.assertEqual(image_processor.crop_size ,				{'''height''': 84, '''width''': 84} )




      def 						snake_case_	(					self						:				int ):
          pass




      def 						snake_case_	(					self						:				Union[str, Any] ):
          __lowercase						:	Dict					=				self.image_processing_class(**self.image_processor_dict )
          # create random PIL images
          __lowercase						:	int					=				prepare_image_inputs(self.image_processor_tester ,				equal_resolution=lowercase_ )
          for image in image_inputs:
              self.assertIsInstance(lowercase_ ,				Image.Image )

          # Test not batched input
          __lowercase						:	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
          __lowercase						:	Tuple					=				image_processing(lowercase_ ,				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 						snake_case_	(					self						:				Optional[Any] ):
          __lowercase						:	int					=				self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          __lowercase						:	Union[str, Any]					=				prepare_image_inputs(self.image_processor_tester ,				equal_resolution=lowercase_ ,				numpify=lowercase_ )
          for image in image_inputs:
              self.assertIsInstance(lowercase_ ,				np.ndarray )

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

          # Test batched
          __lowercase						:	Union[str, Any]					=				image_processing(lowercase_ ,				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 						snake_case_	(					self						:				List[str] ):
          __lowercase						:	Union[str, Any]					=				self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          __lowercase						:	int					=				prepare_image_inputs(self.image_processor_tester ,				equal_resolution=lowercase_ ,				torchify=lowercase_ )
          for image in image_inputs:
              self.assertIsInstance(lowercase_ ,				torch.Tensor )

          # Test not batched input
          __lowercase						:	Tuple					=				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						:	Tuple					=				image_processing(lowercase_ ,				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  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       int,lowercase_ :       Dict=None,lowercase_ :       Tuple=None,lowercase_ :       List[Any]=None,**lowercase_ :       Any		)->    Optional[Any]:





        '''simple docstring'''







        if tokenize_kwargs is None:
            A__          =   {}

        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__          =   truncation

        A__          =   tokenize_kwargs

        A__          =   {}
        if return_tensors is not None:
            A__          =   return_tensors

        return preprocess_params, {}, postprocess_params


    def 						snake_case__     (      self :       Dict,lowercase_ :       List[Any],**lowercase_ :       Tuple		)->    Dict[str, GenericTensor]:





        '''simple docstring'''







        A__          =   self.framework
        A__          =   self.tokenizer(lowercase_,return_tensors=lowercase_,**lowercase_		)
        return model_inputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model(**lowercase_		)
        return model_outputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Tuple,lowercase_ :       List[str]=False		)->    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 :       List[Any],*lowercase_ :       int,**lowercase_ :       Optional[Any]		)->    int:





        '''simple docstring'''







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

