





import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 					UpperCamelCase_    (       UpperCamelCase__			,					unittest.TestCase      ):
							lowerCamelCase_          =   RePaintPipeline
							lowerCamelCase_          =   IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
							lowerCamelCase_          =   PipelineTesterMixin.required_optional_params - {
							    "latents",
							    "num_images_per_prompt",
							    "callback",
							    "callback_steps",
							}
							lowerCamelCase_          =   IMAGE_INPAINTING_BATCH_PARAMS
							lowerCamelCase_          =   False


							def        _snake_case					(							self      :Any						)				->				Tuple:



									"""simple docstring"""



									torch.manual_seed(0						)
									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""")							,			)
									SCREAMING_SNAKE_CASE__								= RePaintScheduler()
									SCREAMING_SNAKE_CASE__								= {"""unet""": unet, """scheduler""": scheduler}
									return components


							def        _snake_case					(							self      :Tuple							,			__A      :str							,			__A      :Optional[Any]=0						)				->				str:



									"""simple docstring"""



									if str(__A						).startswith("""mps"""						):
											SCREAMING_SNAKE_CASE__								= torch.manual_seed(__A						)
									else:
											SCREAMING_SNAKE_CASE__								= torch.Generator(device=__A						).manual_seed(__A						)
									SCREAMING_SNAKE_CASE__								= np.random.RandomState(__A						).standard_normal((1, 3, 32, 32)						)
									SCREAMING_SNAKE_CASE__								= torch.from_numpy(__A						).to(device=__A							,			dtype=torch.floataa						)
									SCREAMING_SNAKE_CASE__								= (image > 0).to(device=__A							,			dtype=torch.floataa						)
									SCREAMING_SNAKE_CASE__								= {
									    """image""": image,
									    """mask_image""": mask,
									    """generator""": generator,
									    """num_inference_steps""": 5,
									    """eta""": 0.0,
									    """jump_length""": 2,
									    """jump_n_sample""": 2,
									    """output_type""": """numpy""",
									}
									return inputs


							def        _snake_case					(							self      :Dict						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= """cpu"""  # ensure determinism for the device-dependent torch.Generator
									SCREAMING_SNAKE_CASE__								= self.get_dummy_components()
									SCREAMING_SNAKE_CASE__								= RePaintPipeline(**__A						)
									SCREAMING_SNAKE_CASE__								= sd_pipe.to(__A						)
									sd_pipe.set_progress_bar_config(disable=__A						)

									SCREAMING_SNAKE_CASE__								= self.get_dummy_inputs(__A						)
									SCREAMING_SNAKE_CASE__								= sd_pipe(**__A						).images
									SCREAMING_SNAKE_CASE__								= image[0, -3:, -3:, -1]

									assert image.shape == (1, 32, 32, 3)
									SCREAMING_SNAKE_CASE__								= np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]						)

									assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3


							@skip_mps
							def        _snake_case					(							self      :List[str]						)				->				List[Any]:



									"""simple docstring"""



									return super().test_save_load_local()


							@unittest.skip("""non-deterministic pipeline"""						)
							def        _snake_case					(							self      :Union[str, Any]						)				->				str:



									"""simple docstring"""



									return super().test_inference_batch_single_identical()


							@skip_mps
							def        _snake_case					(							self      :List[str]						)				->				int:



									"""simple docstring"""



									return super().test_dict_tuple_outputs_equivalent()


							@skip_mps
							def        _snake_case					(							self      :Optional[Any]						)				->				List[str]:



									"""simple docstring"""



									return super().test_save_load_optional_components()


							@skip_mps
							def        _snake_case					(							self      :Union[str, Any]						)				->				List[Any]:



									"""simple docstring"""



									return super().test_attention_slicing_forward_pass()




@nightly
@require_torch_gpu
class 					UpperCamelCase_    (       unittest.TestCase      ):


							def        _snake_case					(							self      :Optional[int]						)				->				Optional[Any]:



									"""simple docstring"""



									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


							def        _snake_case					(							self      :List[Any]						)				->				Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
									    """repaint/celeba_hq_256.png"""						)
									SCREAMING_SNAKE_CASE__								= load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""						)
									SCREAMING_SNAKE_CASE__								= load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
									    """repaint/celeba_hq_256_result.npy"""						)

									SCREAMING_SNAKE_CASE__								= """google/ddpm-ema-celebahq-256"""
									SCREAMING_SNAKE_CASE__								= UNetaDModel.from_pretrained(__A						)
									SCREAMING_SNAKE_CASE__								= RePaintScheduler.from_pretrained(__A						)

									SCREAMING_SNAKE_CASE__								= RePaintPipeline(unet=__A							,			scheduler=__A						).to(__A						)
									repaint.set_progress_bar_config(disable=__A						)
									repaint.enable_attention_slicing()

									SCREAMING_SNAKE_CASE__								= torch.manual_seed(0						)
									SCREAMING_SNAKE_CASE__								= repaint(
									    __A							,			__A							,			num_inference_steps=250							,			eta=0.0							,			jump_length=10							,			jump_n_sample=10							,			generator=__A							,			output_type="""np"""							,			)
									SCREAMING_SNAKE_CASE__								= output.images[0]

									assert image.shape == (256, 256, 3)
									assert np.abs(expected_image - image						).mean() < 1E-2






import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
	from tokenizers import Tokenizer
	from tokenizers.models import WordPiece




class 					UpperCamelCase_    (       unittest.TestCase      ):


							def        _snake_case					(							self      :str							,			__A      :str						)				->				Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= list(tokenizer_class.max_model_input_sizes.keys()						)
									for model_name in sa_models[:1]:
											SCREAMING_SNAKE_CASE__								= tokenizer_class.from_pretrained(__A						)
											self.assertIsNotNone(__A						)
											self.assertIsInstance(__A							,			__A						)
											self.assertIsInstance(__A							,			__A						)

											for special_tok in tokenizer.all_special_tokens:
													self.assertIsInstance(__A							,			__A						)
													SCREAMING_SNAKE_CASE__								= tokenizer.convert_tokens_to_ids(__A						)
													self.assertIsInstance(__A							,			__A						)


							def        _snake_case					(							self      :List[str]							,			__A      :BatchEncoding							,			__A      :Optional[Callable] = None						)				->				Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= pickle.dumps(__A						)
									self.assertIsNotNone(__A						)

									SCREAMING_SNAKE_CASE__								= pickle.loads(__A						)

									# Ensure is_fast is correctly restored
									self.assertEqual(be_restored.is_fast							,			be_original.is_fast						)

									# Ensure encodings are potentially correctly restored
									if be_original.is_fast:
											self.assertIsNotNone(be_restored.encodings						)
									else:
											self.assertIsNone(be_restored.encodings						)

									# Ensure the keys are the same
									for original_v, restored_v in zip(be_original.values()							,			be_restored.values()						):
											if equal_op:
													self.assertTrue(equal_op(__A							,			__A						)						)
											else:
													self.assertEqual(__A							,			__A						)


							@slow
							def        _snake_case					(							self      :Dict						)				->				Optional[int]:



									"""simple docstring"""



									self.check_tokenizer_from_pretrained(__A						)


							def        _snake_case					(							self      :List[Any]						)				->				Dict:



									"""simple docstring"""



									self.assertEqual(TensorType("""tf"""						)							,			TensorType.TENSORFLOW						)
									self.assertEqual(TensorType("""pt"""						)							,			TensorType.PYTORCH						)
									self.assertEqual(TensorType("""np"""						)							,			TensorType.NUMPY						)


							@require_tokenizers
							def        _snake_case					(							self      :Optional[Any]						)				->				List[Any]:



									"""simple docstring"""



									import numpy as np

									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)
									SCREAMING_SNAKE_CASE__								= BertTokenizerFast.from_pretrained("""bert-base-cased"""						)

									# Python no tensor
									with self.subTest("""BatchEncoding (Python, return_tensors=None)"""						):
											self.assert_dump_and_restore(tokenizer_p("""Small example to encode"""						)						)

									with self.subTest("""BatchEncoding (Python, return_tensors=NUMPY)"""						):
											self.assert_dump_and_restore(
											    tokenizer_p("""Small example to encode"""							,			return_tensors=TensorType.NUMPY						)							,			np.array_equal						)

									with self.subTest("""BatchEncoding (Rust, return_tensors=None)"""						):
											self.assert_dump_and_restore(tokenizer_r("""Small example to encode"""						)						)

									with self.subTest("""BatchEncoding (Rust, return_tensors=NUMPY)"""						):
											self.assert_dump_and_restore(
											    tokenizer_r("""Small example to encode"""							,			return_tensors=TensorType.NUMPY						)							,			np.array_equal						)


							@require_tf
							@require_tokenizers
							def        _snake_case					(							self      :Optional[Any]						)				->				Optional[Any]:



									"""simple docstring"""



									import tensorflow as tf

									def tf_array_equals(__A      :Union[str, Any]							,			__A      :Any						):
											return tf.reduce_all(tf.equal(__A							,			__A						)						)

									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)
									SCREAMING_SNAKE_CASE__								= BertTokenizerFast.from_pretrained("""bert-base-cased"""						)

									with self.subTest("""BatchEncoding (Python, return_tensors=TENSORFLOW)"""						):
											self.assert_dump_and_restore(
											    tokenizer_p("""Small example to encode"""							,			return_tensors=TensorType.TENSORFLOW						)							,			__A						)

									with self.subTest("""BatchEncoding (Rust, return_tensors=TENSORFLOW)"""						):
											self.assert_dump_and_restore(
											    tokenizer_r("""Small example to encode"""							,			return_tensors=TensorType.TENSORFLOW						)							,			__A						)


							@require_torch
							@require_tokenizers
							def        _snake_case					(							self      :List[Any]						)				->				int:



									"""simple docstring"""



									import torch

									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)
									SCREAMING_SNAKE_CASE__								= BertTokenizerFast.from_pretrained("""bert-base-cased"""						)

									with self.subTest("""BatchEncoding (Python, return_tensors=PYTORCH)"""						):
											self.assert_dump_and_restore(
											    tokenizer_p("""Small example to encode"""							,			return_tensors=TensorType.PYTORCH						)							,			torch.equal						)

									with self.subTest("""BatchEncoding (Rust, return_tensors=PYTORCH)"""						):
											self.assert_dump_and_restore(
											    tokenizer_r("""Small example to encode"""							,			return_tensors=TensorType.PYTORCH						)							,			torch.equal						)


							@require_tokenizers
							def        _snake_case					(							self      :Optional[Any]						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)
									SCREAMING_SNAKE_CASE__								= BertTokenizerFast.from_pretrained("""bert-base-cased"""						)

									with self.subTest("""Python Tokenizer"""						):
											self.assertFalse(tokenizer_p("""Small example to_encode"""						).is_fast						)

									with self.subTest("""Rust Tokenizer"""						):
											self.assertTrue(tokenizer_r("""Small example to_encode"""						).is_fast						)


							@require_tokenizers
							def        _snake_case					(							self      :List[str]						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BertTokenizerFast.from_pretrained("""bert-base-cased"""						)
									SCREAMING_SNAKE_CASE__								= tokenizer_r(["""Test""", """\xad""", """test"""]							,			is_split_into_words=__A						)

									self.assertEqual(encoded.word_to_tokens(0						)							,			TokenSpan(start=1							,			end=2						)						)
									self.assertEqual(encoded.word_to_tokens(1						)							,			__A						)
									self.assertEqual(encoded.word_to_tokens(2						)							,			TokenSpan(start=2							,			end=3						)						)


							def        _snake_case					(							self      :Tuple						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}						)
									SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""np"""						)
									self.assertEqual(tensor_batch["""inputs"""].shape							,			(2, 3)						)
									self.assertEqual(tensor_batch["""labels"""].shape							,			(2,)						)
									# test converting the converted
									with CaptureStderr() as cs:
											SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""np"""						)
									self.assertFalse(len(cs.err						)							,			msg=f'''should have no warning, but got {cs.err}'''						)

									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}						)
									SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""np"""							,			prepend_batch_axis=__A						)
									self.assertEqual(tensor_batch["""inputs"""].shape							,			(1, 3)						)
									self.assertEqual(tensor_batch["""labels"""].shape							,			(1,)						)


							@require_torch
							def        _snake_case					(							self      :int						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}						)
									SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""pt"""						)
									self.assertEqual(tensor_batch["""inputs"""].shape							,			(2, 3)						)
									self.assertEqual(tensor_batch["""labels"""].shape							,			(2,)						)
									# test converting the converted
									with CaptureStderr() as cs:
											SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""pt"""						)
									self.assertFalse(len(cs.err						)							,			msg=f'''should have no warning, but got {cs.err}'''						)

									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}						)
									SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""pt"""							,			prepend_batch_axis=__A						)
									self.assertEqual(tensor_batch["""inputs"""].shape							,			(1, 3)						)
									self.assertEqual(tensor_batch["""labels"""].shape							,			(1,)						)


							@require_tf
							def        _snake_case					(							self      :Optional[Any]						)				->				Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}						)
									SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""tf"""						)
									self.assertEqual(tensor_batch["""inputs"""].shape							,			(2, 3)						)
									self.assertEqual(tensor_batch["""labels"""].shape							,			(2,)						)
									# test converting the converted
									with CaptureStderr() as cs:
											SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""tf"""						)
									self.assertFalse(len(cs.err						)							,			msg=f'''should have no warning, but got {cs.err}'''						)

									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}						)
									SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""tf"""							,			prepend_batch_axis=__A						)
									self.assertEqual(tensor_batch["""inputs"""].shape							,			(1, 3)						)
									self.assertEqual(tensor_batch["""labels"""].shape							,			(1,)						)


							@require_flax
							def        _snake_case					(							self      :Dict						)				->				Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}						)
									SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""jax"""						)
									self.assertEqual(tensor_batch["""inputs"""].shape							,			(2, 3)						)
									self.assertEqual(tensor_batch["""labels"""].shape							,			(2,)						)
									# test converting the converted
									with CaptureStderr() as cs:
											SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""jax"""						)
									self.assertFalse(len(cs.err						)							,			msg=f'''should have no warning, but got {cs.err}'''						)

									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}						)
									SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""jax"""							,			prepend_batch_axis=__A						)
									self.assertEqual(tensor_batch["""inputs"""].shape							,			(1, 3)						)
									self.assertEqual(tensor_batch["""labels"""].shape							,			(1,)						)


							def        _snake_case					(							self      :List[Any]						)				->				Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= [{"""input_ids""": np.array([0, 1, 2]						)}, {"""input_ids""": np.array([0, 1, 2, 3]						)}]
									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)

									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A						)
									self.assertTrue(isinstance(batch["""input_ids"""]							,			np.ndarray						)						)
									self.assertEqual(batch["""input_ids"""].tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)
									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A							,			return_tensors="""np"""						)
									self.assertTrue(isinstance(batch["""input_ids"""]							,			np.ndarray						)						)
									self.assertEqual(batch["""input_ids"""].tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)


							@require_torch
							def        _snake_case					(							self      :int						)				->				Dict:



									"""simple docstring"""



									import torch

									SCREAMING_SNAKE_CASE__								= [{"""input_ids""": torch.tensor([0, 1, 2]						)}, {"""input_ids""": torch.tensor([0, 1, 2, 3]						)}]
									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)

									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A						)
									self.assertTrue(isinstance(batch["""input_ids"""]							,			torch.Tensor						)						)
									self.assertEqual(batch["""input_ids"""].tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)
									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A							,			return_tensors="""pt"""						)
									self.assertTrue(isinstance(batch["""input_ids"""]							,			torch.Tensor						)						)
									self.assertEqual(batch["""input_ids"""].tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)


							@require_tf
							def        _snake_case					(							self      :int						)				->				int:



									"""simple docstring"""



									import tensorflow as tf

									SCREAMING_SNAKE_CASE__								= [{"""input_ids""": tf.constant([0, 1, 2]						)}, {"""input_ids""": tf.constant([0, 1, 2, 3]						)}]
									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)

									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A						)
									self.assertTrue(isinstance(batch["""input_ids"""]							,			tf.Tensor						)						)
									self.assertEqual(batch["""input_ids"""].numpy().tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)
									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A							,			return_tensors="""tf"""						)
									self.assertTrue(isinstance(batch["""input_ids"""]							,			tf.Tensor						)						)
									self.assertEqual(batch["""input_ids"""].numpy().tolist()							,			[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]						)


							@require_tokenizers
							def        _snake_case					(							self      :Tuple						)				->				Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= Tokenizer(WordPiece(unk_token="""[UNK]"""						)						)
									PreTrainedTokenizerFast(tokenizer_object=__A						)


							@require_tokenizers
							def        _snake_case					(							self      :Any						)				->				Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= Tokenizer(WordPiece(unk_token="""[UNK]"""						)						)
									with tempfile.TemporaryDirectory() as tmpdirname:
											bert_tokenizer.save(os.path.join(__A							,			"""tokenizer.json"""						)						)
											PreTrainedTokenizerFast(tokenizer_file=os.path.join(__A							,			"""tokenizer.json"""						)						)