
"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class  snake_case     (							unittest.TestCase   ):

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = UNetaDModel(
									    block_out_channels=(32, 64)       ,       layers_per_block=2       ,       sample_size=32       ,       in_channels=3       ,       out_channels=3       ,       down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')       ,       up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')       ,       )
									return model







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.dummy_uncond_unet
									SCREAMING_SNAKE_CASE_						 = ScoreSdeVeScheduler()

									SCREAMING_SNAKE_CASE_						 = ScoreSdeVePipeline(unet=SCREAMING_SNAKE_CASE_       ,       scheduler=SCREAMING_SNAKE_CASE_    )
									sde_ve.to(SCREAMING_SNAKE_CASE_    )
									sde_ve.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = sde_ve(num_inference_steps=2       ,       output_type='''numpy'''       ,       generator=SCREAMING_SNAKE_CASE_    ).images

									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = sde_ve(num_inference_steps=2       ,       output_type='''numpy'''       ,       generator=SCREAMING_SNAKE_CASE_       ,       return_dict=SCREAMING_SNAKE_CASE_    )[
									    0
									]

									SCREAMING_SNAKE_CASE_						 = image[0, -3:, -3:, -1]
									SCREAMING_SNAKE_CASE_						 = image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									SCREAMING_SNAKE_CASE_						 = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2







@slow
@require_torch
class  snake_case     (							unittest.TestCase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = '''google/ncsnpp-church-256'''
									SCREAMING_SNAKE_CASE_						 = UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = ScoreSdeVeScheduler.from_pretrained(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = ScoreSdeVePipeline(unet=SCREAMING_SNAKE_CASE_       ,       scheduler=SCREAMING_SNAKE_CASE_    )
									sde_ve.to(SCREAMING_SNAKE_CASE_    )
									sde_ve.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = sde_ve(num_inference_steps=10       ,       output_type='''numpy'''       ,       generator=SCREAMING_SNAKE_CASE_    ).images

									SCREAMING_SNAKE_CASE_						 = image[0, -3:, -3:, -1]

									assert image.shape == (1, 2_56, 2_56, 3)

									SCREAMING_SNAKE_CASE_						 = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2

"""simple docstring"""




import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class  snake_case     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      CTRLTokenizer
							UpperCAmelCase__						     =      False
							UpperCAmelCase__						     =      False

							def _lowercase					(self    ):

									"""simple docstring"""







									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									SCREAMING_SNAKE_CASE_						 = ['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
									SCREAMING_SNAKE_CASE_						 = dict(zip(SCREAMING_SNAKE_CASE_       ,       range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
									SCREAMING_SNAKE_CASE_						 = ['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
									SCREAMING_SNAKE_CASE_						 = {'''unk_token''': '''<unk>'''}

									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''vocab_file''']    )
									SCREAMING_SNAKE_CASE_						 = os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES['''merges_file''']    )
									with open(self.vocab_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as fp:
											fp.write(json.dumps(SCREAMING_SNAKE_CASE_    ) + '''\n'''    )
									with open(self.merges_file       ,       '''w'''       ,       encoding='''utf-8'''    ) as fp:
											fp.write('''\n'''.join(SCREAMING_SNAKE_CASE_    )    )

							def _lowercase					(self       ,       **SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									kwargs.update(self.special_tokens_map    )
									return CTRLTokenizer.from_pretrained(self.tmpdirname       ,       **SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = '''adapt react readapt apt'''
									SCREAMING_SNAKE_CASE_						 = '''adapt react readapt apt'''
									return input_text, output_text







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = CTRLTokenizer(self.vocab_file       ,       self.merges_file       ,       **self.special_tokens_map    )
									SCREAMING_SNAKE_CASE_						 = '''adapt react readapt apt'''
									SCREAMING_SNAKE_CASE_						 = '''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
									SCREAMING_SNAKE_CASE_						 = tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
									self.assertListEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = tokens + [tokenizer.unk_token]

									SCREAMING_SNAKE_CASE_						 = [0, 1, 2, 4, 5, 1, 0, 3, 6]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )       ,       SCREAMING_SNAKE_CASE_    )