





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				,	UpperCAmelCase_				,	UpperCAmelCase_=7				,	UpperCAmelCase_=3				,	UpperCAmelCase_=18				,	UpperCAmelCase_=30				,	UpperCAmelCase_=400				,	UpperCAmelCase_=True				,	UpperCAmelCase_=None				,	UpperCAmelCase_=True				,	):
								lowerCamelCase			=size if size is not None else {"height": 18, "width": 18}
								lowerCamelCase			=parent
								lowerCamelCase			=batch_size
								lowerCamelCase			=num_channels
								lowerCamelCase			=image_size
								lowerCamelCase			=min_resolution
								lowerCamelCase			=max_resolution
								lowerCamelCase			=do_resize
								lowerCamelCase			=size
								lowerCamelCase			=do_normalize


				def _snake_case    (						self							):
								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       (  a      ,   unittest.TestCase      ):
				UpperCAmelCase_								=			ImageGPTImageProcessor if is_vision_available() else None


				def _snake_case    (						self							):
								lowerCamelCase			=ImageGPTImageProcessingTester(self							)


				@property
				def _snake_case    (						self							):
								return self.image_processor_tester.prepare_image_processor_dict()


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

								lowerCamelCase			=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							):
								lowerCamelCase			=self.image_processing_class(**self.image_processor_dict							)
								lowerCamelCase			=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							):
								lowerCamelCase			=self.image_processing_class(**self.image_processor_dict							)

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

								lowerCamelCase			=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							):
								lowerCamelCase			=self.image_processing_class(**self.image_processor_dict							)

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

								lowerCamelCase			=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							):
								pass





def        _lowercase   (  )  ->					Union[str, Any]:
				lowerCamelCase			=load_dataset("""hf-internal-testing/fixtures_image_utils"""     , split="""test"""			)

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

				lowerCamelCase			=[imagea, imagea]

				return images




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


				@slow
				def _snake_case    (						self							):
								lowerCamelCase			=ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""							)

								lowerCamelCase			=prepare_images()

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

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

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

								# test batched
								lowerCamelCase			=image_processing(_a				,	return_tensors="""pt"""							)

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

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












import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class  __A       (  a      ,   a      ,   unittest.TestCase      ):
				__A								=			IFInpaintingSuperResolutionPipeline
				__A								=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
				__A								=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}      )
				__A								=			PipelineTesterMixin.required_optional_params - {"""latents"""}


				def _snake_case    (						self							):
								return self._get_superresolution_dummy_components()


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_=0							):
								if str(UpperCAmelCase_							).startswith("""mps"""							):
												lowerCamelCase			=torch.manual_seed(UpperCAmelCase_							)
								else:
												lowerCamelCase			=torch.Generator(device=UpperCAmelCase_							).manual_seed(UpperCAmelCase_							)

								lowerCamelCase			=floats_tensor((1, 3, 16, 16)				,	rng=random.Random(UpperCAmelCase_							)							).to(UpperCAmelCase_							)
								lowerCamelCase			=floats_tensor((1, 3, 32, 32)				,	rng=random.Random(UpperCAmelCase_							)							).to(UpperCAmelCase_							)
								lowerCamelCase			=floats_tensor((1, 3, 32, 32)				,	rng=random.Random(UpperCAmelCase_							)							).to(UpperCAmelCase_							)

								lowerCamelCase			={
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """image""": image,
								    """original_image""": original_image,
								    """mask_image""": mask_image,
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """output_type""": """numpy""",
								}

								return inputs


				@unittest.skipIf(
				    torch_device != """cuda""" or not is_xformers_available()				,	reason="""XFormers attention is only available with CUDA and `xformers` installed"""				,	)
				def _snake_case    (						self							):
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)


				def _snake_case    (						self							):
								self._test_save_load_optional_components()


				@unittest.skipIf(torch_device != """cuda"""				,	reason="""float16 requires CUDA"""							)
				def _snake_case    (						self							):
								# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
								super().test_save_load_floataa(expected_max_diff=1E-1							)


				def _snake_case    (						self							):
								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)


				def _snake_case    (						self							):
								self._test_save_load_local()


				def _snake_case    (						self							):
								self._test_inference_batch_single_identical(
								    expected_max_diff=1E-2				,	)





