"""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						:	str ,					__lowerCAmelCase						:	List[str] ,					__lowerCAmelCase						:	str=7 ,					__lowerCAmelCase						:	Dict=3 ,					__lowerCAmelCase						:	int=18 ,					__lowerCAmelCase						:	str=30 ,					__lowerCAmelCase						:	Tuple=400 ,					__lowerCAmelCase						:	List[Any]=True ,					__lowerCAmelCase						:	Any=None ,					__lowerCAmelCase						:	Dict=True ,					):
      _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   lowerCAmelCase_	(       self						:	Any       ):
      return {
          # here we create 2 clusters for the sake of simplicity
          "clusters": np.asarray(
              [
                  [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
                  [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
              ]       ),
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
      }



@require_torch
@require_vision
class 							a					(		lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	List[Any]  				=				ImageGPTImageProcessor if is_vision_available() else None


    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   ImageGPTImageProcessingTester(self       )


    @property
    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      return self.image_processor_tester.prepare_image_processor_dict()


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	Dict       ):
      _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   lowerCAmelCase_	(       self						:	str       ):
      _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(__lowerCAmelCase ,					obj[key]       )       )
        else:
          self.assertEqual(obj[key] ,					__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	Optional[Any]       ):
      _UpperCAmelCase    			=   self.image_processing_class(**self.image_processor_dict       )

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

      _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	Tuple       ):
      _UpperCAmelCase    			=   self.image_processing_class(**self.image_processor_dict       )

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

      _UpperCAmelCase    			=   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   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      pass






def      __UpperCAmelCase       (      ):







  """simple docstring"""
  _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   lowerCAmelCase_	(       self						:	Dict       ):
      _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() ,					__lowerCAmelCase       )

      # test batched
      _UpperCAmelCase    			=   image_processing(__lowerCAmelCase ,					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() ,					__lowerCAmelCase       )

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase__			=    {"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ViTMAEForPreTraining""",
        """ViTMAELayer""",
        """ViTMAEModel""",
        """ViTMAEPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__			=    [
        """TFViTMAEForPreTraining""",
        """TFViTMAEModel""",
        """TFViTMAEPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vit_mae import (
            VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
            ViTMAEForPreTraining,
            ViTMAELayer,
            ViTMAEModel,
            ViTMAEPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
    import sys

    UpperCAmelCase__			=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
