




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       , UpperCAmelCase_       , UpperCAmelCase_=7       , UpperCAmelCase_=3       , UpperCAmelCase_=18       , UpperCAmelCase_=30       , UpperCAmelCase_=4_00       , 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(
											        [
											            [0.8866443634033203, 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			( __UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Optional[Any]										=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(UpperCAmelCase_       , '''clusters'''						)						)
											self.assertTrue(hasattr(UpperCAmelCase_       , '''do_resize'''						)						)
											self.assertTrue(hasattr(UpperCAmelCase_       , '''size'''						)						)
											self.assertTrue(hasattr(UpperCAmelCase_       , '''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(UpperCAmelCase_       , obj[key]						)						)
																		else:
																									self.assertEqual(obj[key]       , UpperCAmelCase_						)



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)

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

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



				def       __snake_case		( self						):
											lowerCAmelCase   =		self.image_processing_class(**self.image_processor_dict						)

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

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






				@unittest.skip('''ImageGPT requires clusters at initialization'''						)
				def       __snake_case		( self						):
											pass




def       UpperCAmelCase       (     ):
							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 							__UpperCamelCase			( unittest.TestCase  ):





				'''simple docstring'''



				@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, 10_24)						)

											lowerCAmelCase   =		[3_06, 1_91, 1_91]
											self.assertEqual(encoding.input_ids[0, :3].tolist()       , UpperCAmelCase_						)

											# test batched
											lowerCAmelCase   =		image_processing(UpperCAmelCase_       , return_tensors='''pt'''						)

											self.assertIsInstance(encoding.input_ids       , torch.LongTensor						)
											self.assertEqual(encoding.input_ids.shape       , (2, 10_24)						)

											lowerCAmelCase   =		[3_03, 13, 13]
											self.assertEqual(encoding.input_ids[1, -3:].tolist()       , UpperCAmelCase_						)









# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin


class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Any										=1



				@register_to_config
				def __init__( self       , UpperCAmelCase_=20_00       , UpperCAmelCase_=0.1       , UpperCAmelCase_=20       , UpperCAmelCase_=1E-3						):
											lowerCAmelCase   =		None
											lowerCAmelCase   =		None
											lowerCAmelCase   =		None



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None						):
											lowerCAmelCase   =		torch.linspace(1       , self.config.sampling_eps       , UpperCAmelCase_       , device=UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_=None						):
											if self.timesteps is None:
																		raise ValueError(
																		    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

											# TODO(Patrick) better comments + non-PyTorch
											# postprocess model score
											lowerCAmelCase   =		(
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											lowerCAmelCase   =		torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff						)						)
											lowerCAmelCase   =		std.flatten()
											while len(std.shape						) < len(score.shape						):
																		lowerCAmelCase   =		std.unsqueeze(-1						)
											lowerCAmelCase   =		-score / std

											# compute
											lowerCAmelCase   =		-1.0 / len(self.timesteps						)

											lowerCAmelCase   =		self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											lowerCAmelCase   =		beta_t.flatten()
											while len(beta_t.shape						) < len(x.shape						):
																		lowerCAmelCase   =		beta_t.unsqueeze(-1						)
											lowerCAmelCase   =		-0.5 * beta_t * x

											lowerCAmelCase   =		torch.sqrt(UpperCAmelCase_						)
											lowerCAmelCase   =		drift - diffusion**2 * score
											lowerCAmelCase   =		x + drift * dt

											# add noise
											lowerCAmelCase   =		randn_tensor(x.shape       , layout=x.layout       , generator=UpperCAmelCase_       , device=x.device       , dtype=x.dtype						)
											lowerCAmelCase   =		x_mean + diffusion * math.sqrt(-dt						) * noise

											return x, x_mean






				def __len__( self						):
											return self.config.num_train_timesteps



