"""simple docstring"""




import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
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 ImageGPTImageProcessor
class 						__A							(			unittest.TestCase		):






     def __init__(		self    ,					a__    ,					a__=7    ,					a__=3    ,					a__=18    ,					a__=30    ,					a__=400    ,					a__=True    ,					a__=None    ,					a__=True    ,					):
       _lowerCAmelCase						:	Optional[Any]        =					size if size is not None else {"""height""": 18, """width""": 18}
       _lowerCAmelCase						:	Optional[int]        =					parent
       _lowerCAmelCase						:	List[str]        =					batch_size
       _lowerCAmelCase						:	Optional[int]        =					num_channels
       _lowerCAmelCase						:	Optional[int]        =					image_size
       _lowerCAmelCase						:	int        =					min_resolution
       _lowerCAmelCase						:	int        =					max_resolution
       _lowerCAmelCase						:	Tuple        =					do_resize
       _lowerCAmelCase						:	Optional[int]        =					size
       _lowerCAmelCase						:	Optional[Any]        =					do_normalize






     def    __A  (		self  ):
       return {
           # here we create 2 clusters for the sake of simplicity
           "clusters": np.asarray(
               [
                   [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
                   [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
               ]  ),
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
       }







@require_torch
@require_vision
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      Optional[Any]		 =		ImageGPTImageProcessor if is_vision_available() else None






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					ImageGPTImageProcessingTester(self  )






     @property
     def    __A  (		self  ):
       return self.image_processor_tester.prepare_image_processor_dict()






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(a__    ,					"""clusters"""  )  )
       self.assertTrue(hasattr(a__    ,					"""do_resize"""  )  )
       self.assertTrue(hasattr(a__    ,					"""size"""  )  )
       self.assertTrue(hasattr(a__    ,					"""do_normalize"""  )  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[Any]        =					self.image_processing_class.from_dict(self.image_processor_dict  )
       self.assertEqual(image_processor.size    ,					{"""height""": 18, """width""": 18}  )

       _lowerCAmelCase						:	Any        =					self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42  )
       self.assertEqual(image_processor.size    ,					{"""height""": 42, """width""": 42}  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	int        =					self.image_processing_class(**self.image_processor_dict  )
       _lowerCAmelCase						:	Optional[Any]        =					json.loads(image_processor.to_json_string()  )
       for key, value in self.image_processor_dict.items():
         if key == "clusters":
           self.assertTrue(np.array_equal(a__    ,					obj[key]  )  )
         else:
           self.assertEqual(obj[key]    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					self.image_processing_class(**self.image_processor_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
         _lowerCAmelCase						:	Optional[int]        =					os.path.join(a__    ,					"""image_processor.json"""  )
         image_processor_first.to_json_file(a__  )
         _lowerCAmelCase						:	Any        =					self.image_processing_class.from_json_file(a__  ).to_dict()

       _lowerCAmelCase						:	Optional[Any]        =					image_processor_first.to_dict()
       for key, value in image_processor_first.items():
         if key == "clusters":
           self.assertTrue(np.array_equal(a__    ,					image_processor_second[key]  )  )
         else:
           self.assertEqual(image_processor_first[key]    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Tuple        =					self.image_processing_class(**self.image_processor_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
         image_processor_first.save_pretrained(a__  )
         _lowerCAmelCase						:	Optional[Any]        =					self.image_processing_class.from_pretrained(a__  ).to_dict()

       _lowerCAmelCase						:	Union[str, Any]        =					image_processor_first.to_dict()
       for key, value in image_processor_first.items():
         if key == "clusters":
           self.assertTrue(np.array_equal(a__    ,					image_processor_second[key]  )  )
         else:
           self.assertEqual(image_processor_first[key]    ,					a__  )






     @unittest.skip("""ImageGPT requires clusters at initialization"""  )
     def    __A  (		self  ):
       pass







def 							SCREAMING_SNAKE_CASE					(      )	->				Optional[Any]:
  _lowerCAmelCase						:	int        =					load_dataset("""hf-internal-testing/fixtures_image_utils"""      ,split="""test"""     )

  _lowerCAmelCase						:	Any        =					Image.open(dataset[4]["""file"""]     )
  _lowerCAmelCase						:	str        =					Image.open(dataset[5]["""file"""]     )

  _lowerCAmelCase						:	str        =					[imagea, imagea]

  return images






@require_vision
@require_torch
class 						__A							(			unittest.TestCase		):






     @slow
     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""  )

       _lowerCAmelCase						:	List[Any]        =					prepare_images()

       # test non-batched
       _lowerCAmelCase						:	Tuple        =					image_processing(images[0]    ,					return_tensors="""pt"""  )

       self.assertIsInstance(encoding.input_ids    ,					torch.LongTensor  )
       self.assertEqual(encoding.input_ids.shape    ,					(1, 1024)  )

       _lowerCAmelCase						:	Optional[int]        =					[306, 191, 191]
       self.assertEqual(encoding.input_ids[0, :3].tolist()    ,					a__  )

       # test batched
       _lowerCAmelCase						:	Optional[int]        =					image_processing(a__    ,					return_tensors="""pt"""  )

       self.assertIsInstance(encoding.input_ids    ,					torch.LongTensor  )
       self.assertEqual(encoding.input_ids.shape    ,					(2, 1024)  )

       _lowerCAmelCase						:	List[str]        =					[303, 13, 13]
       self.assertEqual(encoding.input_ids[1, -3:].tolist()    ,					a__  )





"""simple docstring"""




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 PoolFormerImageProcessor
class 						__A							(			unittest.TestCase		):






     def __init__(		self    ,					a__    ,					a__=7    ,					a__=3    ,					a__=30    ,					a__=400    ,					a__=True    ,					a__=None    ,					a__=0.9    ,					a__=None    ,					a__=True    ,					a__=[0.5, 0.5, 0.5]    ,					a__=[0.5, 0.5, 0.5]    ,					):
       _lowerCAmelCase						:	int        =					size if size is not None else {"""shortest_edge""": 30}
       _lowerCAmelCase						:	Dict        =					crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
       _lowerCAmelCase						:	List[Any]        =					parent
       _lowerCAmelCase						:	Optional[int]        =					batch_size
       _lowerCAmelCase						:	Any        =					num_channels
       _lowerCAmelCase						:	str        =					min_resolution
       _lowerCAmelCase						:	Dict        =					max_resolution
       _lowerCAmelCase						:	str        =					do_resize_and_center_crop
       _lowerCAmelCase						:	List[str]        =					size
       _lowerCAmelCase						:	int        =					crop_pct
       _lowerCAmelCase						:	int        =					crop_size
       _lowerCAmelCase						:	Union[str, Any]        =					do_normalize
       _lowerCAmelCase						:	Tuple        =					image_mean
       _lowerCAmelCase						:	Optional[Any]        =					image_std






     def    __A  (		self  ):
       return {
           "size": self.size,
           "do_resize_and_center_crop": self.do_resize_and_center_crop,
           "crop_pct": self.crop_pct,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }







@require_torch
@require_vision
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      List[str]		 =		PoolFormerImageProcessor if is_vision_available() else None






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					PoolFormerImageProcessingTester(self  )






     @property
     def    __A  (		self  ):
       return self.image_processor_tester.prepare_image_processor_dict()






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(a__    ,					"""do_resize_and_center_crop"""  )  )
       self.assertTrue(hasattr(a__    ,					"""size"""  )  )
       self.assertTrue(hasattr(a__    ,					"""crop_pct"""  )  )
       self.assertTrue(hasattr(a__    ,					"""do_normalize"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_mean"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_std"""  )  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class.from_dict(self.image_processor_dict  )
       self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 30}  )
       self.assertEqual(image_processor.crop_size    ,					{"""height""": 30, """width""": 30}  )

       _lowerCAmelCase						:	List[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    __A  (		self  ):
       pass






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       _lowerCAmelCase						:	Tuple        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					Image.Image  )

       # Test not batched input
       _lowerCAmelCase						:	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
       _lowerCAmelCase						:	List[str]        =					image_processing(a__    ,					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    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Union[str, Any]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       _lowerCAmelCase						:	Union[str, Any]        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__    ,					numpify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					np.ndarray  )

       # Test not batched input
       _lowerCAmelCase						:	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
       _lowerCAmelCase						:	int        =					image_processing(a__    ,					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    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Any        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       _lowerCAmelCase						:	Any        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__    ,					torchify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					torch.Tensor  )

       # Test not batched input
       _lowerCAmelCase						:	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						:	List[str]        =					image_processing(a__    ,					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"""],
           )    ,					)




