


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 			lowerCamelCase_		(			unittest.TestCase   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__=7       ,		UpperCAmelCase__=3       ,		UpperCAmelCase__=18       ,		UpperCAmelCase__=30       ,		UpperCAmelCase__=400       ,		UpperCAmelCase__=True       ,		UpperCAmelCase__=None       ,		UpperCAmelCase__=True       ,		):
								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   lowerCAmelCase__		(						self     ):
								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 			lowerCamelCase_		(			__lowercase       ,  unittest.TestCase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Optional[Any]      							=					ImageGPTImageProcessor if is_vision_available() else None



				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				ImageGPTImageProcessingTester(self     )



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



				def   lowerCAmelCase__		(						self     ):
								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   lowerCAmelCase__		(						self     ):
								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   lowerCAmelCase__		(						self     ):
								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   lowerCAmelCase__		(						self     ):
								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   lowerCAmelCase__		(						self     ):
								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   lowerCAmelCase__		(						self     ):
								pass


def __lowercase     (      ):
				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 			lowerCamelCase_		(			unittest.TestCase   ):





				"""simple docstring"""







				@slow
				def   lowerCAmelCase__		(						self     ):
								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     )




"""simple docstring"""






# 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 			lowerCamelCase_		(			lowercase       ,  lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					1



				@register_to_config
				def __init__(						self       ,		UpperCAmelCase__=2000       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=20       ,		UpperCAmelCase__=1e-3     ):
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								SCREAMING_SNAKE_CASE__     							=				torch.linspace(1       ,		self.config.sampling_eps       ,		UpperCAmelCase__       ,		device=UpperCAmelCase__     )



				def   lowerCAmelCase__		(						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
								SCREAMING_SNAKE_CASE__     							=				(
								    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
								)
								SCREAMING_SNAKE_CASE__     							=				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff     )     )
								SCREAMING_SNAKE_CASE__     							=				std.flatten()
								while len(std.shape     ) < len(score.shape     ):
												SCREAMING_SNAKE_CASE__     							=				std.unsqueeze(-1     )
								SCREAMING_SNAKE_CASE__     							=				-score / std

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

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

								SCREAMING_SNAKE_CASE__     							=				torch.sqrt(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				drift - diffusion**2 * score
								SCREAMING_SNAKE_CASE__     							=				x + drift * dt

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

								return x, x_mean






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