





'''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 		UpperCAmelCase   (				unittest.TestCase):


		"""simple docstring"""







		def __init__(				self    :					Any							,	UpperCamelCase__    :					List[str]							,	UpperCamelCase__    :					str=7							,	UpperCamelCase__    :					Optional[Any]=3							,	UpperCamelCase__    :					Tuple=18							,	UpperCamelCase__    :					List[Any]=30							,	UpperCamelCase__    :					Tuple=400							,	UpperCamelCase__    :					Tuple=True							,	UpperCamelCase__    :					Any=None							,	UpperCamelCase__    :					Any=True							,	)     ->						Optional[Any]:
				_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   UpperCamelCase__      (				self    :					Dict  )     ->						List[str]:
				return {
				    # here we create 2 clusters for the sake of simplicity
				    "clusters": np.asarray(
				        [
				            [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
				            [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
				        ]  ),
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				}







@require_torch
@require_vision
class 		UpperCAmelCase   (				snake_case__   ,     unittest.TestCase):


		"""simple docstring"""







		lowerCAmelCase_					=				ImageGPTImageProcessor if is_vision_available() else None


		def   UpperCamelCase__      (				self    :					Any  )     ->						Tuple:
				_UpperCamelCase       			=ImageGPTImageProcessingTester(self  )


		@property
		def   UpperCamelCase__      (				self    :					Any  )     ->						Any:
				return self.image_processor_tester.prepare_image_processor_dict()


		def   UpperCamelCase__      (				self    :					int  )     ->						Union[str, Any]:
				_UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )
				self.assertTrue(hasattr(lowercase_							,	'''clusters'''  )  )
				self.assertTrue(hasattr(lowercase_							,	'''do_resize'''  )  )
				self.assertTrue(hasattr(lowercase_							,	'''size'''  )  )
				self.assertTrue(hasattr(lowercase_							,	'''do_normalize'''  )  )


		def   UpperCamelCase__      (				self    :					List[Any]  )     ->						List[Any]:
				_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   UpperCamelCase__      (				self    :					str  )     ->						Dict:
				_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(lowercase_							,	obj[key]  )  )
						else:
								self.assertEqual(obj[key]							,	lowercase_  )


		def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Tuple:
				_UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )

				with tempfile.TemporaryDirectory() as tmpdirname:
						_UpperCamelCase       			=os.path.join(lowercase_							,	'''image_processor.json'''  )
						image_processor_first.to_json_file(lowercase_  )
						_UpperCamelCase       			=self.image_processing_class.from_json_file(lowercase_  ).to_dict()

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


		def   UpperCamelCase__      (				self    :					Dict  )     ->						int:
				_UpperCamelCase       			=self.image_processing_class(**self.image_processor_dict  )

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

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




		@unittest.skip('''ImageGPT requires clusters at initialization'''  )
		def   UpperCamelCase__      (				self    :					Any  )     ->						Dict:
				pass




def 					_a   ():


		"""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 		UpperCAmelCase   (				unittest.TestCase):


		"""simple docstring"""







		@slow
		def   UpperCamelCase__      (				self    :					List[Any]  )     ->						str:
				_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()							,	lowercase_  )

				# test batched
				_UpperCamelCase       			=image_processing(lowercase_							,	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()							,	lowercase_  )













'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase						: Optional[int]		=   {
    'configuration_xmod': [
        'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XmodConfig',
        'XmodOnnxConfig',
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: int		=   [
          'XMOD_PRETRAINED_MODEL_ARCHIVE_LIST',
          'XmodForCausalLM',
          'XmodForMaskedLM',
          'XmodForMultipleChoice',
          'XmodForQuestionAnswering',
          'XmodForSequenceClassification',
          'XmodForTokenClassification',
          'XmodModel',
          'XmodPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_xmod import (
                XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
                XmodForCausalLM,
                XmodForMaskedLM,
                XmodForMultipleChoice,
                XmodForQuestionAnswering,
                XmodForSequenceClassification,
                XmodForTokenClassification,
                XmodModel,
                XmodPreTrainedModel,
            )

else:
      import sys

      __lowerCamelCase						: Dict		=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






