


'''simple docstring'''
import unittest

from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase						=					get_tests_dir("""fixtures/spiece.model""")






@require_sentencepiece
@require_tokenizers
class        lowerCAmelCase_  (     UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=DebertaVaTokenizer
       __UpperCAmelCase					=DebertaVaTokenizerFast
       __UpperCAmelCase					=True
       __UpperCAmelCase					=True






       def   UpperCamelCase						( self   )->      Optional[int]:
            super().setUp()

            # We have a SentencePiece fixture for testing
            _A				=							DebertaVaTokenizer(_UpperCamelCase			,     unk_token='<unk>'   )
            tokenizer.save_pretrained(self.tmpdirname   )






       def   UpperCamelCase						( self			,     _UpperCamelCase   )->      List[str]:
            _A				=							'this is a test'
            _A				=							'this is a test'
            return input_text, output_text






       def   UpperCamelCase						( self   )->      int:
            _A				=							'<pad>'
            _A				=							0

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCamelCase   )			,     _UpperCamelCase   )
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCamelCase   )			,     _UpperCamelCase   )






       def   UpperCamelCase						( self   )->      str:
            _A				=							list(self.get_tokenizer().get_vocab().keys()   )
            self.assertEqual(vocab_keys[0]			,     '<pad>'   )
            self.assertEqual(vocab_keys[1]			,     '<unk>'   )
            self.assertEqual(vocab_keys[-1]			,     '[PAD]'   )
            self.assertEqual(len(_UpperCamelCase   )			,     3_0001   )






       def   UpperCamelCase						( self   )->      Optional[int]:
            self.assertEqual(self.get_tokenizer().vocab_size			,     3_0000   )






       def   UpperCamelCase						( self   )->      Optional[int]:
            # fmt: off
            _A				=							' \tHeLLo!how  \n Are yoU?  '
            _A				=							['▁hello', '!', 'how', '▁are', '▁you', '?']
            # fmt: on

            _A				=							DebertaVaTokenizer(_UpperCamelCase			,     do_lower_case=_UpperCamelCase   )
            _A				=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )   )

            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )

            _A				=							DebertaVaTokenizerFast(_UpperCamelCase			,     do_lower_case=_UpperCamelCase   )
            _A				=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )   )

            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )






       @unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.'   )
       def   UpperCamelCase						( self   )->      Any:
            pass






       @unittest.skip('There is an inconsistency between slow and fast tokenizer due to a bug in the fast one.'   )
       def   UpperCamelCase						( self   )->      List[Any]:
            pass






       def   UpperCamelCase						( self   )->      List[Any]:
            # fmt: off
            _A				=							'I was born in 92000, and this is falsé.'
            _A				=							['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
            # fmt: on

            _A				=							DebertaVaTokenizer(_UpperCamelCase			,     split_by_punct=_UpperCamelCase   )
            _A				=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )   )

            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )

            _A				=							DebertaVaTokenizerFast(_UpperCamelCase			,     split_by_punct=_UpperCamelCase   )
            _A				=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )   )

            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )






       def   UpperCamelCase						( self   )->      int:
            # fmt: off
            _A				=							'I was born in 92000, and this is falsé.'
            _A				=							['▁i', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
            # fmt: on

            _A				=							DebertaVaTokenizer(_UpperCamelCase			,     do_lower_case=_UpperCamelCase			,     split_by_punct=_UpperCamelCase   )
            _A				=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )

            _A				=							DebertaVaTokenizerFast(_UpperCamelCase			,     do_lower_case=_UpperCamelCase			,     split_by_punct=_UpperCamelCase   )
            _A				=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )






       def   UpperCamelCase						( self   )->      Dict:
            # fmt: off
            _A				=							'I was born in 92000, and this is falsé.'
            _A				=							['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
            # fmt: on

            _A				=							DebertaVaTokenizer(_UpperCamelCase			,     do_lower_case=_UpperCamelCase			,     split_by_punct=_UpperCamelCase   )
            _A				=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )   )

            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )

            _A				=							DebertaVaTokenizerFast(_UpperCamelCase			,     do_lower_case=_UpperCamelCase			,     split_by_punct=_UpperCamelCase   )
            _A				=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )   )

            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )






       def   UpperCamelCase						( self   )->      List[str]:
            # fmt: off
            _A				=							'I was born in 92000, and this is falsé.'
            _A				=							['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', '▁', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '▁', '.', ]
            # fmt: on

            _A				=							DebertaVaTokenizer(_UpperCamelCase			,     do_lower_case=_UpperCamelCase			,     split_by_punct=_UpperCamelCase   )
            _A				=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )   )

            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )

            _A				=							DebertaVaTokenizerFast(_UpperCamelCase			,     do_lower_case=_UpperCamelCase			,     split_by_punct=_UpperCamelCase   )
            _A				=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )   )

            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )






       def   UpperCamelCase						( self   )->      int:
            # fmt: off
            _A				=							' \tHeLLo!how  \n Are yoU?  '
            _A				=							['▁', '<unk>', 'e', '<unk>', 'o', '!', 'how', '▁', '<unk>', 're', '▁yo', '<unk>', '?']
            # fmt: on

            _A				=							DebertaVaTokenizer(_UpperCamelCase			,     do_lower_case=_UpperCamelCase			,     split_by_punct=_UpperCamelCase   )
            _A				=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )   )

            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )

            _A				=							DebertaVaTokenizerFast(_UpperCamelCase			,     do_lower_case=_UpperCamelCase			,     split_by_punct=_UpperCamelCase   )
            _A				=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )   )

            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )






       def   UpperCamelCase						( self   )->      Tuple:
            _A				=							self.get_tokenizer()
            _A				=							self.get_rust_tokenizer()

            _A				=							'I was born in 92000, and this is falsé.'

            _A				=							tokenizer.convert_ids_to_tokens(tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )   )
            _A				=							rust_tokenizer.convert_ids_to_tokens(rust_tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )

            _A				=							tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )
            _A				=							rust_tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )

            _A				=							self.get_rust_tokenizer()
            _A				=							tokenizer.encode(_UpperCamelCase   )
            _A				=							rust_tokenizer.encode(_UpperCamelCase   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )






       def   UpperCamelCase						( self   )->      str:
            _A				=							'This is a test'
            _A				=							[13, 1, 4398, 25, 21, 1289]
            _A				=							['▁', 'T', 'his', '▁is', '▁a', '▁test']
            _A				=							['▁', '<unk>', 'his', '▁is', '▁a', '▁test']

            _A				=							DebertaVaTokenizer(_UpperCamelCase			,     keep_accents=_UpperCamelCase   )
            _A				=							DebertaVaTokenizerFast(_UpperCamelCase			,     keep_accents=_UpperCamelCase   )

            _A				=							tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )
            _A				=							tokenizer.tokenize(_UpperCamelCase   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )
            _A				=							tokenizer.convert_ids_to_tokens(_UpperCamelCase   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )

            _A				=							rust_tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )
            _A				=							rust_tokenizer.tokenize(_UpperCamelCase   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )
            _A				=							rust_tokenizer.convert_ids_to_tokens(_UpperCamelCase   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )

            # fmt: off
            _A				=							'I was born in 92000, and this is falsé.'
            _A				=							[13, 1, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]
            _A				=							['▁', 'I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.', ]
            _A				=							['▁', '<unk>', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.', ]
            # fmt: on

            _A				=							tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )
            _A				=							tokenizer.tokenize(_UpperCamelCase   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )
            _A				=							tokenizer.convert_ids_to_tokens(_UpperCamelCase   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )

            _A				=							rust_tokenizer.encode(_UpperCamelCase			,     add_special_tokens=_UpperCamelCase   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )
            _A				=							rust_tokenizer.tokenize(_UpperCamelCase   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )
            _A				=							rust_tokenizer.convert_ids_to_tokens(_UpperCamelCase   )
            self.assertListEqual(_UpperCamelCase			,     _UpperCamelCase   )






       def   UpperCamelCase						( self   )->      List[Any]:
            _A				=							DebertaVaTokenizer(_UpperCamelCase   )

            _A				=							tokenizer.encode('sequence builders'   )
            _A				=							tokenizer.encode('multi-sequence build'   )

            _A				=							tokenizer.build_inputs_with_special_tokens(_UpperCamelCase   )
            _A				=							tokenizer.build_inputs_with_special_tokens(_UpperCamelCase			,     _UpperCamelCase   )

            self.assertEqual([tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]			,     _UpperCamelCase   )
            self.assertEqual(
                [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [tokenizer.sep_token_id]			,     _UpperCamelCase			,     )






       @slow
       def   UpperCamelCase						( self   )->      Optional[Any]:
            # fmt: off
            _A				=							{'input_ids': [[1, 3_9867, 36, 1_9390, 486, 27, 3_5052, 8_1436, 18, 6_0685, 1225, 7, 3_5052, 8_1436, 18, 9367, 1_6899, 18, 1_5937, 53, 594, 773, 18, 1_6287, 3_0465, 36, 1_5937, 6, 4_1139, 38, 3_6979, 6_0763, 191, 6, 3_4132, 99, 6, 5_0538, 390, 4_3230, 6, 3_4132, 2779, 2_0850, 14, 699, 1072, 1194, 36, 382, 1_0901, 53, 7, 699, 1072, 2084, 36, 2_0422, 630, 53, 19, 105, 3049, 1896, 1053, 1_6899, 1506, 11, 3_7978, 4243, 7, 1237, 3_1869, 200, 1_6566, 654, 6, 3_5052, 8_1436, 7, 5_5630, 1_3593, 4, 2], [1, 26, 1_5011, 13, 667, 8, 1053, 18, 2_3611, 1237, 7_2356, 1_2820, 34, 10_4134, 1209, 35, 1_3313, 6627, 21, 202, 347, 7, 164, 2399, 11, 46, 4485, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 5, 1232, 2864, 1_5785, 1_4951, 105, 5, 8581, 1250, 4, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
            # fmt: on

            self.tokenizer_integration_test_util(
                expected_encoding=_UpperCamelCase			,     model_name='microsoft/deberta-v2-xlarge'			,     revision='ad6e42c1532ddf3a15c39246b63f5559d558b670'			,     )









'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class        lowerCAmelCase_  (     UpperCAmelCase      ,							UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=CycleDiffusionPipeline
       __UpperCAmelCase					=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
           'negative_prompt',
           'height',
           'width',
           'negative_prompt_embeds',
       }
       __UpperCAmelCase					=PipelineTesterMixin.required_optional_params - {'latents'}
       __UpperCAmelCase					=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}	)
       __UpperCAmelCase					=IMAGE_TO_IMAGE_IMAGE_PARAMS
       __UpperCAmelCase					=IMAGE_TO_IMAGE_IMAGE_PARAMS






       def   UpperCamelCase						( self   )->      Dict:
            torch.manual_seed(0   )
            _A				=							UNetaDConditionModel(
                block_out_channels=(32, 64)			,     layers_per_block=2			,     sample_size=32			,     in_channels=4			,     out_channels=4			,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,     cross_attention_dim=32			,     )
            _A				=							DDIMScheduler(
                beta_start=0.0_0085			,     beta_end=0.012			,     beta_schedule='scaled_linear'			,     num_train_timesteps=1000			,     clip_sample=_UpperCamelCase			,     set_alpha_to_one=_UpperCamelCase			,     )
            torch.manual_seed(0   )
            _A				=							AutoencoderKL(
                block_out_channels=[32, 64]			,     in_channels=3			,     out_channels=3			,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']			,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']			,     latent_channels=4			,     )
            torch.manual_seed(0   )
            _A				=							CLIPTextConfig(
                bos_token_id=0			,     eos_token_id=2			,     hidden_size=32			,     intermediate_size=37			,     layer_norm_eps=1e-05			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=1000			,     )
            _A				=							CLIPTextModel(_UpperCamelCase   )
            _A				=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

            _A				=							{
                'unet': unet,
                'scheduler': scheduler,
                'vae': vae,
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'safety_checker': None,
                'feature_extractor': None,
            }
            return components






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase=0   )->      List[Any]:
            _A				=							floats_tensor((1, 3, 32, 32)			,     rng=random.Random(_UpperCamelCase   )   ).to(_UpperCamelCase   )
            _A				=							image / 2 + 0.5
            if str(_UpperCamelCase   ).startswith('mps'   ):
                 _A				=							torch.manual_seed(_UpperCamelCase   )
            else:
                 _A				=							torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )
            _A				=							{
                'prompt': 'An astronaut riding an elephant',
                'source_prompt': 'An astronaut riding a horse',
                'image': image,
                'generator': generator,
                'num_inference_steps': 2,
                'eta': 0.1,
                'strength': 0.8,
                'guidance_scale': 3,
                'source_guidance_scale': 1,
                'output_type': 'numpy',
            }
            return inputs






       def   UpperCamelCase						( self   )->      Any:
            _A				=							'cpu'  # ensure determinism for the device-dependent torch.Generator

            _A				=							self.get_dummy_components()
            _A				=							CycleDiffusionPipeline(**_UpperCamelCase   )
            _A				=							pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            _A				=							self.get_dummy_inputs(_UpperCamelCase   )
            _A				=							pipe(**_UpperCamelCase   )
            _A				=							output.images

            _A				=							images[0, -3:, -3:, -1]

            assert images.shape == (1, 32, 32, 3)
            _A				=							np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]   )

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






       @unittest.skipIf(torch_device != 'cuda'			,     'This test requires a GPU'   )
       def   UpperCamelCase						( self   )->      List[Any]:
            _A				=							self.get_dummy_components()
            for name, module in components.items():
                 if hasattr(_UpperCamelCase			,     'half'   ):
                      _A				=							module.half()
            _A				=							CycleDiffusionPipeline(**_UpperCamelCase   )
            _A				=							pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            _A				=							self.get_dummy_inputs(_UpperCamelCase   )
            _A				=							pipe(**_UpperCamelCase   )
            _A				=							output.images

            _A				=							images[0, -3:, -3:, -1]

            assert images.shape == (1, 32, 32, 3)
            _A				=							np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]   )

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






       @skip_mps
       def   UpperCamelCase						( self   )->      Any:
            return super().test_save_load_local()






       @unittest.skip('non-deterministic pipeline'   )
       def   UpperCamelCase						( self   )->      List[Any]:
            return super().test_inference_batch_single_identical()






       @skip_mps
       def   UpperCamelCase						( self   )->      int:
            return super().test_dict_tuple_outputs_equivalent()






       @skip_mps
       def   UpperCamelCase						( self   )->      Tuple:
            return super().test_save_load_optional_components()






       @skip_mps
       def   UpperCamelCase						( self   )->      Optional[Any]:
            return super().test_attention_slicing_forward_pass()






@slow
@require_torch_gpu
class        lowerCAmelCase_  (     unittest.TestCase	):






       def   UpperCamelCase						( self   )->      Any:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()






       def   UpperCamelCase						( self   )->      int:
            _A				=							load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/cycle-diffusion/black_colored_car.png'   )
            _A				=							load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'   )
            _A				=							init_image.resize((512, 512)   )

            _A				=							'CompVis/stable-diffusion-v1-4'
            _A				=							DDIMScheduler.from_pretrained(_UpperCamelCase			,     subfolder='scheduler'   )
            _A				=							CycleDiffusionPipeline.from_pretrained(
                _UpperCamelCase			,     scheduler=_UpperCamelCase			,     safety_checker=_UpperCamelCase			,     torch_dtype=torch.floataa			,     revision='fp16'   )

            pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )
            pipe.enable_attention_slicing()

            _A				=							'A black colored car'
            _A				=							'A blue colored car'

            _A				=							torch.manual_seed(0   )
            _A				=							pipe(
                prompt=_UpperCamelCase			,     source_prompt=_UpperCamelCase			,     image=_UpperCamelCase			,     num_inference_steps=100			,     eta=0.1			,     strength=0.85			,     guidance_scale=3			,     source_guidance_scale=1			,     generator=_UpperCamelCase			,     output_type='np'			,     )
            _A				=							output.images

            # the values aren't exactly equal, but the images look the same visually
            assert np.abs(image - expected_image   ).max() < 5e-1






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            _A				=							load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/cycle-diffusion/black_colored_car.png'   )
            _A				=							load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'   )
            _A				=							init_image.resize((512, 512)   )

            _A				=							'CompVis/stable-diffusion-v1-4'
            _A				=							DDIMScheduler.from_pretrained(_UpperCamelCase			,     subfolder='scheduler'   )
            _A				=							CycleDiffusionPipeline.from_pretrained(_UpperCamelCase			,     scheduler=_UpperCamelCase			,     safety_checker=_UpperCamelCase   )

            pipe.to(_UpperCamelCase   )
            pipe.set_progress_bar_config(disable=_UpperCamelCase   )
            pipe.enable_attention_slicing()

            _A				=							'A black colored car'
            _A				=							'A blue colored car'

            _A				=							torch.manual_seed(0   )
            _A				=							pipe(
                prompt=_UpperCamelCase			,     source_prompt=_UpperCamelCase			,     image=_UpperCamelCase			,     num_inference_steps=100			,     eta=0.1			,     strength=0.85			,     guidance_scale=3			,     source_guidance_scale=1			,     generator=_UpperCamelCase			,     output_type='np'			,     )
            _A				=							output.images

            assert np.abs(image - expected_image   ).max() < 2e-2





