
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 							_UpperCamelCase					(       unittest.TestCase	):






			'''simple docstring'''

			@property
			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							torch.manual_seed(0   )
							UpperCamelCase_:				Any						=    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    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Tuple						=    self.dummy_uncond_unet
							UpperCamelCase_:				str						=    ScoreSdeVeScheduler()

							UpperCamelCase_:				str						=    ScoreSdeVePipeline(unet=_lowercase    ,						scheduler=_lowercase   )
							sde_ve.to(_lowercase   )
							sde_ve.set_progress_bar_config(disable=_lowercase   )

							UpperCamelCase_:				Optional[int]						=    torch.manual_seed(0   )
							UpperCamelCase_:				List[str]						=    sde_ve(num_inference_steps=2    ,						output_type="""numpy"""    ,						generator=_lowercase   ).images

							UpperCamelCase_:				List[Any]						=    torch.manual_seed(0   )
							UpperCamelCase_:				Tuple						=    sde_ve(num_inference_steps=2    ,						output_type="""numpy"""    ,						generator=_lowercase    ,						return_dict=_lowercase   )[
							    0
							]

							UpperCamelCase_:				Optional[int]						=    image[0, -3:, -3:, -1]
							UpperCamelCase_:				Dict						=    image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase_:				Optional[Any]						=    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 							_UpperCamelCase					(       unittest.TestCase	):






			'''simple docstring'''

			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							UpperCamelCase_:				Optional[Any]						=    """google/ncsnpp-church-256"""
							UpperCamelCase_:				Tuple						=    UNetaDModel.from_pretrained(_lowercase   )

							UpperCamelCase_:				Any						=    ScoreSdeVeScheduler.from_pretrained(_lowercase   )

							UpperCamelCase_:				Any						=    ScoreSdeVePipeline(unet=_lowercase    ,						scheduler=_lowercase   )
							sde_ve.to(_lowercase   )
							sde_ve.set_progress_bar_config(disable=_lowercase   )

							UpperCamelCase_:				List[Any]						=    torch.manual_seed(0   )
							UpperCamelCase_:				Dict						=    sde_ve(num_inference_steps=10    ,						output_type="""numpy"""    ,						generator=_lowercase   ).images

							UpperCamelCase_:				Optional[int]						=    image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)

							UpperCamelCase_:				int						=    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   __UpperCAmelCase				(     lowerCAmelCase			,unittest.TestCase				):



					'''simple docstring'''





					_UpperCamelCase							=		CTRLTokenizer
					_UpperCamelCase							=		False
					_UpperCamelCase							=		False



					def     __snake_case						(	self     :						str)    ->							List[Any]:
								super().setUp()

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

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



					def     __snake_case						(	self     :						Optional[int]      ,       **_lowercase     :						Optional[int])    ->							Union[str, Any]:
								kwargs.update(self.special_tokens_map)
								return CTRLTokenizer.from_pretrained(self.tmpdirname      ,       **_lowercase)



					def     __snake_case						(	self     :						List[Any]      ,       _lowercase     :						Union[str, Any])    ->							Dict:
								A_									= 'adapt react readapt apt'
								A_									= 'adapt react readapt apt'
								return input_text, output_text



					def     __snake_case						(	self     :						Any)    ->							Any:
								A_									= CTRLTokenizer(self.vocab_file      ,       self.merges_file      ,       **self.special_tokens_map)
								A_									= 'adapt react readapt apt'
								A_									= 'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
								A_									= tokenizer.tokenize(_lowercase)
								self.assertListEqual(_lowercase      ,       _lowercase)

								A_									= tokens + [tokenizer.unk_token]

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




