






'''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      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=7      ,		_SCREAMING_SNAKE_CASE=3      ,		_SCREAMING_SNAKE_CASE=18      ,		_SCREAMING_SNAKE_CASE=30      ,		_SCREAMING_SNAKE_CASE=400      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=True      ,		)  ->				List[Any]:



           """simple docstring"""






           UpperCamelCase      = size if 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_normalize


     def   A__						(				self				)  ->				int:



           """simple docstring"""






           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_						(      __UpperCamelCase     ,	unittest.TestCase    ):
     lowercase							=				ImageGPTImageProcessor if is_vision_available() else None

     def   A__						(				self				)  ->				Optional[int]:



           """simple docstring"""






           UpperCamelCase      = ImageGPTImageProcessingTester(self				)

     @property
     def   A__						(				self				)  ->				str:



           """simple docstring"""






           return self.image_processor_tester.prepare_image_processor_dict()

     def   A__						(				self				)  ->				List[str]:



           """simple docstring"""






           UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""clusters"""				)				)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""do_resize"""				)				)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""size"""				)				)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""do_normalize"""				)				)

     def   A__						(				self				)  ->				List[Any]:



           """simple docstring"""






           UpperCamelCase      = self.image_processing_class.from_dict(self.image_processor_dict				)
           self.assertEqual(image_processor.size      ,		{"""height""": 18, """width""": 18}				)

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

     def   A__						(				self				)  ->				Any:



           """simple docstring"""






           UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
           UpperCamelCase      = json.loads(image_processor.to_json_string()				)
           for key, value in self.image_processor_dict.items():
                 if key == "clusters":
                       self.assertTrue(np.array_equal(_SCREAMING_SNAKE_CASE      ,		obj[key]				)				)
                 else:
                       self.assertEqual(obj[key]      ,		_SCREAMING_SNAKE_CASE				)

     def   A__						(				self				)  ->				str:



           """simple docstring"""






           UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)

           with tempfile.TemporaryDirectory() as tmpdirname:
                 UpperCamelCase      = os.path.join(_SCREAMING_SNAKE_CASE      ,		"""image_processor.json"""				)
                 image_processor_first.to_json_file(_SCREAMING_SNAKE_CASE				)
                 UpperCamelCase      = self.image_processing_class.from_json_file(_SCREAMING_SNAKE_CASE				).to_dict()

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

     def   A__						(				self				)  ->				str:



           """simple docstring"""






           UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)

           with tempfile.TemporaryDirectory() as tmpdirname:
                 image_processor_first.save_pretrained(_SCREAMING_SNAKE_CASE				)
                 UpperCamelCase      = self.image_processing_class.from_pretrained(_SCREAMING_SNAKE_CASE				).to_dict()

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


     @unittest.skip("""ImageGPT requires clusters at initialization"""				)
     def   A__						(				self				)  ->				List[Any]:



           """simple docstring"""






           pass







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

      UpperCamelCase      = Image.open(dataset[4]["""file"""]   )
      UpperCamelCase      = Image.open(dataset[5]["""file"""]   )

      UpperCamelCase      = [imagea, imagea]

      return images






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

     @slow
     def   A__						(				self				)  ->				int:



           """simple docstring"""






           UpperCamelCase      = ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""				)

           UpperCamelCase      = prepare_images()

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

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

           UpperCamelCase      = [306, 191, 191]
           self.assertEqual(encoding.input_ids[0, :3].tolist()      ,		_SCREAMING_SNAKE_CASE				)

           # test batched
           UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)

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

           UpperCamelCase      = [303, 13, 13]
           self.assertEqual(encoding.input_ids[1, -3:].tolist()      ,		_SCREAMING_SNAKE_CASE				)








'''simple docstring'''

import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)






class     a_						(      unittest.TestCase    ):

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = ["""a""", """b""", """c"""]

        # Defaults to last layer if both are None
        UpperCamelCase  ,UpperCamelCase      = get_aligned_output_features_output_indices(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		["""c"""]				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		[2]				)

        # Out indices set to match out features
        UpperCamelCase  ,UpperCamelCase      = get_aligned_output_features_output_indices(["""a""", """c"""]      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		["""a""", """c"""]				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		[0, 2]				)

        # Out features set to match out indices
        UpperCamelCase  ,UpperCamelCase      = get_aligned_output_features_output_indices(_SCREAMING_SNAKE_CASE      ,		[0, 2]      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		["""a""", """c"""]				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		[0, 2]				)

        # Out features selected from negative indices
        UpperCamelCase  ,UpperCamelCase      = get_aligned_output_features_output_indices(_SCREAMING_SNAKE_CASE      ,		[-3, -1]      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		["""a""", """c"""]				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		[-3, -1]				)

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        with self.assertRaises(_SCREAMING_SNAKE_CASE				):
              verify_out_features_out_indices(["""a""", """b"""]      ,		(0, 1)      ,		_SCREAMING_SNAKE_CASE				)

        # Out features must be a list
        with self.assertRaises(_SCREAMING_SNAKE_CASE				):
              verify_out_features_out_indices(("""a""", """b""")      ,		(0, 1)      ,		["""a""", """b"""]				)

        # Out features must be a subset of stage names
        with self.assertRaises(_SCREAMING_SNAKE_CASE				):
              verify_out_features_out_indices(["""a""", """b"""]      ,		(0, 1)      ,		["""a"""]				)

        # Out indices must be a list or tuple
        with self.assertRaises(_SCREAMING_SNAKE_CASE				):
              verify_out_features_out_indices(_SCREAMING_SNAKE_CASE      ,		0      ,		["""a""", """b"""]				)

        # Out indices must be a subset of stage names
        with self.assertRaises(_SCREAMING_SNAKE_CASE				):
              verify_out_features_out_indices(_SCREAMING_SNAKE_CASE      ,		(0, 1)      ,		["""a"""]				)

        # Out features and out indices must be the same length
        with self.assertRaises(_SCREAMING_SNAKE_CASE				):
              verify_out_features_out_indices(["""a""", """b"""]      ,		(0,)      ,		["""a""", """b""", """c"""]				)

        # Out features should match out indices
        with self.assertRaises(_SCREAMING_SNAKE_CASE				):
              verify_out_features_out_indices(["""a""", """b"""]      ,		(0, 2)      ,		["""a""", """b""", """c"""]				)

        # Out features and out indices should be in order
        with self.assertRaises(_SCREAMING_SNAKE_CASE				):
              verify_out_features_out_indices(["""b""", """a"""]      ,		(0, 1)      ,		["""a""", """b"""]				)

        # Check passes with valid inputs
        verify_out_features_out_indices(["""a""", """b""", """d"""]      ,		(0, 1, -1)      ,		["""a""", """b""", """c""", """d"""]				)


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



        """simple docstring"""






        UpperCamelCase      = BackboneMixin()

        UpperCamelCase      = ["""a""", """b""", """c"""]
        UpperCamelCase      = ["""a""", """c"""]
        UpperCamelCase      = [0, 2]

        # Check that the output features and indices are set correctly
        self.assertEqual(backbone.out_features      ,		["""a""", """c"""]				)
        self.assertEqual(backbone.out_indices      ,		[0, 2]				)

        # Check out features and indices are updated correctly
        UpperCamelCase      = ["""a""", """b"""]
        self.assertEqual(backbone.out_features      ,		["""a""", """b"""]				)
        self.assertEqual(backbone.out_indices      ,		[0, 1]				)

        UpperCamelCase      = [-3, -1]
        self.assertEqual(backbone.out_features      ,		["""a""", """c"""]				)
        self.assertEqual(backbone.out_indices      ,		[-3, -1]				)
