





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    ):


							def __init__(							self    :List[str]							,				__A    :List[str]							,				__A    :Union[str, Any]=7							,				__A    :Union[str, Any]=3							,				__A    :List[Any]=18							,				__A    :Optional[int]=30							,				__A    :Tuple=400							,				__A    :Optional[Any]=True							,				__A    :List[Any]=None							,				__A    :Tuple=True							,				)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      size if size is not None else {"""height""": 18, """width""": 18}
									SCREAMING_SNAKE_CASE__			=      parent
									SCREAMING_SNAKE_CASE__			=      batch_size
									SCREAMING_SNAKE_CASE__			=      num_channels
									SCREAMING_SNAKE_CASE__			=      image_size
									SCREAMING_SNAKE_CASE__			=      min_resolution
									SCREAMING_SNAKE_CASE__			=      max_resolution
									SCREAMING_SNAKE_CASE__			=      do_resize
									SCREAMING_SNAKE_CASE__			=      size
									SCREAMING_SNAKE_CASE__			=      do_normalize


							def      _snake_case  (							self    :Any		)   ->   List[str]:



									"""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   UpperCamelCase_       (     UpperCamelCase__				,  unittest.TestCase    ):
							lowerCamelCase_           =					ImageGPTImageProcessor if is_vision_available() else None


							def      _snake_case  (							self    :Optional[Any]		)   ->   Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      ImageGPTImageProcessingTester(self		)


							@property
							def      _snake_case  (							self    :Tuple		)   ->   Union[str, Any]:



									"""simple docstring"""



									return self.image_processor_tester.prepare_image_processor_dict()


							def      _snake_case  (							self    :str		)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      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      _snake_case  (							self    :Tuple		)   ->   int:



									"""simple docstring"""



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

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


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.image_processing_class(**self.image_processor_dict		)
									SCREAMING_SNAKE_CASE__			=      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      _snake_case  (							self    :Any		)   ->   Any:



									"""simple docstring"""



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

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

									SCREAMING_SNAKE_CASE__			=      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      _snake_case  (							self    :int		)   ->   int:



									"""simple docstring"""



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

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

									SCREAMING_SNAKE_CASE__			=      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      _snake_case  (							self    :List[Any]		)   ->   int:



									"""simple docstring"""



									pass






def 				SCREAMING_SNAKE_CASE__							(       ):
		SCREAMING_SNAKE_CASE__			=      load_dataset("""hf-internal-testing/fixtures_image_utils"""					,   split="""test"""    )

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

		SCREAMING_SNAKE_CASE__			=      [imagea, imagea]

		return images




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


							@slow
							def      _snake_case  (							self    :Optional[int]		)   ->   int:



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__			=      prepare_images()

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

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

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

									# test batched
									SCREAMING_SNAKE_CASE__			=      image_processing(__A							,				return_tensors="""pt"""		)

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

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






# 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   				=			{
    'configuration_xmod': [
        'XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XmodConfig',
        'XmodOnnxConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			[
	    '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   				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)