





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     __lowerCAmelCase	(   unittest.TestCase):







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__=7		,				lowerCAmelCase__=3		,				lowerCAmelCase__=1_8		,				lowerCAmelCase__=3_0		,				lowerCAmelCase__=4_0_0		,				lowerCAmelCase__=True		,				lowerCAmelCase__=None		,				lowerCAmelCase__=True		,				)       ->						Dict:



        '''simple docstring'''




        a__     : List[str]  =size if size is not None else {"height": 1_8, "width": 1_8}
        a__     : Dict  =parent
        a__     : int  =batch_size
        a__     : Dict  =num_channels
        a__     : Any  =image_size
        a__     : Union[str, Any]  =min_resolution
        a__     : Union[str, Any]  =max_resolution
        a__     : Dict  =do_resize
        a__     : Tuple  =size
        a__     : str  =do_normalize







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return {
            # here we create 2 clusters for the sake of simplicity
            "clusters": np.asarray(
                [
                    [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
                    [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
                ]							),
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
        }







@require_torch
@require_vision
class     __lowerCAmelCase	(   UpperCamelCase__						,    unittest.TestCase):
      _lowercase    : List[Any]            =      ImageGPTImageProcessor if is_vision_available() else None







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : List[str]  =ImageGPTImageProcessingTester(self							)







      @property
      def        _lowercase  (   self							)       ->						List[str]:



        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()







      def        _lowercase  (   self							)       ->						Tuple:



        '''simple docstring'''




        a__     : str  =self.image_processing_class(**self.image_processor_dict							)
        self.assertTrue(hasattr(lowerCAmelCase__		,				"clusters"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,				"do_resize"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,				"size"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,				"do_normalize"							)							)







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        a__     : str  =self.image_processing_class.from_dict(self.image_processor_dict							)
        self.assertEqual(image_processor.size		,				{"height": 1_8, "width": 1_8}							)

        a__     : Dict  =self.image_processing_class.from_dict(self.image_processor_dict		,				size=4_2							)
        self.assertEqual(image_processor.size		,				{"height": 4_2, "width": 4_2}							)







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



        '''simple docstring'''




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







      def        _lowercase  (   self							)       ->						Optional[Any]:



        '''simple docstring'''




        a__     : Tuple  =self.image_processing_class(**self.image_processor_dict							)

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

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







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



        '''simple docstring'''




        a__     : List[str]  =self.image_processing_class(**self.image_processor_dict							)

        with tempfile.TemporaryDirectory() as tmpdirname:
          image_processor_first.save_pretrained(lowerCAmelCase__							)
          a__     : List[str]  =self.image_processing_class.from_pretrained(lowerCAmelCase__							).to_dict()

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







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



        '''simple docstring'''




        pass
def 					_A	(		):





  """simple docstring"""



  a__     : Any  =load_dataset("hf-internal-testing/fixtures_image_utils"	,	split="test"						)

  a__     : Tuple  =Image.open(dataset[4]["file"]						)
  a__     : Union[str, Any]  =Image.open(dataset[5]["file"]						)

  a__     : List[str]  =[imagea, imagea]

  return images







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







      @slow
      def        _lowercase  (   self							)       ->						List[Any]:



        '''simple docstring'''




        a__     : List[str]  =ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"							)

        a__     : str  =prepare_images()

        # test non-batched
        a__     : List[str]  =image_processing(images[0]		,				return_tensors="pt"							)

        self.assertIsInstance(encoding.input_ids		,				torch.LongTensor							)
        self.assertEqual(encoding.input_ids.shape		,				(1, 1_0_2_4)							)

        a__     : Tuple  =[3_0_6, 1_9_1, 1_9_1]
        self.assertEqual(encoding.input_ids[0, :3].tolist()		,				lowerCAmelCase__							)

        # test batched
        a__     : int  =image_processing(lowerCAmelCase__		,				return_tensors="pt"							)

        self.assertIsInstance(encoding.input_ids		,				torch.LongTensor							)
        self.assertEqual(encoding.input_ids.shape		,				(2, 1_0_2_4)							)

        a__     : Any  =[3_0_3, 1_3, 1_3]
        self.assertEqual(encoding.input_ids[1, -3:].tolist()		,				lowerCAmelCase__							)













from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader







class     __lowerCAmelCase	(   UpperCamelCase__):







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						int:



        '''simple docstring'''




        super().__init__(
            lowerCAmelCase__		,				split=lowerCAmelCase__		,				features=lowerCAmelCase__		,				cache_dir=lowerCAmelCase__		,				keep_in_memory=lowerCAmelCase__		,				streaming=lowerCAmelCase__		,				num_proc=lowerCAmelCase__		,				**lowerCAmelCase__		,				)
        a__     : Tuple  =path_or_paths if isinstance(lowerCAmelCase__		,				lowerCAmelCase__							) else {self.split: path_or_paths}
        a__     : List[str]  =Text(
            cache_dir=lowerCAmelCase__		,				data_files=lowerCAmelCase__		,				features=lowerCAmelCase__		,				**lowerCAmelCase__		,				)







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



        '''simple docstring'''




        if self.streaming:
          a__     : str  =self.builder.as_streaming_dataset(split=self.split							)
        # Build regular (map-style) dataset
        else:
          a__     : Dict  =None
          a__     : Optional[Any]  =None
          a__     : Union[str, Any]  =None
          a__     : Tuple  =None

          self.builder.download_and_prepare(
              download_config=lowerCAmelCase__		,				download_mode=lowerCAmelCase__		,				verification_mode=lowerCAmelCase__		,				base_path=lowerCAmelCase__		,				num_proc=self.num_proc		,				)
          a__     : Tuple  =self.builder.as_dataset(
              split=self.split		,				verification_mode=lowerCAmelCase__		,				in_memory=self.keep_in_memory							)
        return dataset






