
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 MobileNetVaImageProcessor
class       _lowerCAmelCase  (		unittest.TestCase							):

 """simple docstring"""

 def __init__(						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			str=7					, SCREAMING_SNAKE_CASE__			:			List[Any]=3					, SCREAMING_SNAKE_CASE__			:			Dict=18					, SCREAMING_SNAKE_CASE__			:			Optional[int]=30					, SCREAMING_SNAKE_CASE__			:			List[Any]=4_00					, SCREAMING_SNAKE_CASE__			:			List[Any]=True					, SCREAMING_SNAKE_CASE__			:			Dict=None					, SCREAMING_SNAKE_CASE__			:			List[Any]=True					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=None					, ):






      """simple docstring"""



      UpperCamelCase						    =					size if size is not None else {'shortest_edge': 20}
      UpperCamelCase						    =					crop_size if crop_size is not None else {'height': 18, 'width': 18}
      UpperCamelCase						    =					parent
      UpperCamelCase						    =					batch_size
      UpperCamelCase						    =					num_channels
      UpperCamelCase						    =					image_size
      UpperCamelCase						    =					min_resolution
      UpperCamelCase						    =					max_resolution
      UpperCamelCase						    =					do_resize
      UpperCamelCase						    =					size
      UpperCamelCase						    =					do_center_crop
      UpperCamelCase						    =					crop_size






 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }




@require_torch
@require_vision
class       _lowerCAmelCase  (		__magic_name__			,	unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					List[Any]       						=MobileNetVaImageProcessor if is_vision_available() else None

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					MobileNetVaImageProcessingTester(self   )

 @property
 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					self.image_processing_class(**self.image_processor_dict   )
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					, 'do_resize'   )   )
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					, 'size'   )   )
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					, 'do_center_crop'   )   )
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__					, 'crop_size'   )   )

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					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}   )

      UpperCamelCase						    =					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      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.image_processing_class(**self.image_processor_dict   )
      # create random PIL images
      UpperCamelCase						    =					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
      UpperCamelCase						    =					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
      UpperCamelCase						    =					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			:			Optional[int]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.image_processing_class(**self.image_processor_dict   )
      # create random numpy tensors
      UpperCamelCase						    =					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
      UpperCamelCase						    =					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
      UpperCamelCase						    =					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   ):






      """simple docstring"""



      UpperCamelCase						    =					self.image_processing_class(**self.image_processor_dict   )
      # create random PyTorch tensors
      UpperCamelCase						    =					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
      UpperCamelCase						    =					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
      UpperCamelCase						    =					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'],
          )					, )




import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format
class       _lowerCAmelCase  :

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=None

 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.feature_extraction_class(**self.feat_extract_dict   )
      UpperCamelCase						    =					json.loads(feat_extract.to_json_string()   )
      for key, value in self.feat_extract_dict.items():
           self.assertEqual(obj[key]					, SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					self.feature_extraction_class(**self.feat_extract_dict   )

      with tempfile.TemporaryDirectory() as tmpdirname:
           UpperCamelCase						    =					os.path.join(SCREAMING_SNAKE_CASE__					, 'feat_extract.json'   )
           feat_extract_first.to_json_file(SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					self.feature_extraction_class.from_json_file(SCREAMING_SNAKE_CASE__   )

      self.assertEqual(feat_extract_second.to_dict()					, feat_extract_first.to_dict()   )

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					self.feature_extraction_class(**self.feat_extract_dict   )

      with tempfile.TemporaryDirectory() as tmpdirname:
           UpperCamelCase						    =					feat_extract_first.save_pretrained(SCREAMING_SNAKE_CASE__   )[0]
           check_json_file_has_correct_format(SCREAMING_SNAKE_CASE__   )
           UpperCamelCase						    =					self.feature_extraction_class.from_pretrained(SCREAMING_SNAKE_CASE__   )

      self.assertEqual(feat_extract_second.to_dict()					, feat_extract_first.to_dict()   )






 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.feature_extraction_class()
      self.assertIsNotNone(SCREAMING_SNAKE_CASE__   )


