



"""simple docstring"""







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	(     snake_case					,   unittest.TestCase       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       int						       =  RePaintPipeline
	SCREAMING_SNAKE_CASE_							:       Dict						       =  IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
	SCREAMING_SNAKE_CASE_							:       str						       =  PipelineTesterMixin.required_optional_params - {
	    "latents",
	    "num_images_per_prompt",
	    "callback",
	    "callback_steps",
	}
	SCREAMING_SNAKE_CASE_							:       List[Any]						       =  IMAGE_INPAINTING_BATCH_PARAMS
	SCREAMING_SNAKE_CASE_							:       Tuple						       =  False






	def    lowerCamelCase__					(     self   ):
			torch.manual_seed(0   )
			torch.manual_seed(0   )
			_lowercase				: List[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""")				,)
			_lowercase				: str     =   RePaintScheduler()
			_lowercase				: Union[str, Any]     =   {"""unet""": unet, """scheduler""": scheduler}
			return components






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_=0   ):
			if str(UpperCAmelCase_   ).startswith("""mps"""   ):
					_lowercase				: Any     =   torch.manual_seed(UpperCAmelCase_   )
			else:
					_lowercase				: List[str]     =   torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
			_lowercase				: List[Any]     =   np.random.RandomState(UpperCAmelCase_   ).standard_normal((1, 3, 32, 32)   )
			_lowercase				: int     =   torch.from_numpy(UpperCAmelCase_   ).to(device=UpperCAmelCase_				,dtype=torch.floataa   )
			_lowercase				: int     =   (image > 0).to(device=UpperCAmelCase_				,dtype=torch.floataa   )
			_lowercase				: int     =   {
			    """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    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   """cpu"""  # ensure determinism for the device-dependent torch.Generator
			_lowercase				: Dict     =   self.get_dummy_components()
			_lowercase				: Any     =   RePaintPipeline(**UpperCAmelCase_   )
			_lowercase				: Optional[Any]     =   sd_pipe.to(UpperCAmelCase_   )
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

			_lowercase				: Optional[int]     =   self.get_dummy_inputs(UpperCAmelCase_   )
			_lowercase				: List[Any]     =   sd_pipe(**UpperCAmelCase_   ).images
			_lowercase				: Optional[Any]     =   image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			_lowercase				: Optional[int]     =   np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]   )

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






	@skip_mps
	def    lowerCamelCase__					(     self   ):
			return super().test_save_load_local()






	@unittest.skip("""non-deterministic pipeline"""   )
	def    lowerCamelCase__					(     self   ):
			return super().test_inference_batch_single_identical()






	@skip_mps
	def    lowerCamelCase__					(     self   ):
			return super().test_dict_tuple_outputs_equivalent()






	@skip_mps
	def    lowerCamelCase__					(     self   ):
			return super().test_save_load_optional_components()






	@skip_mps
	def    lowerCamelCase__					(     self   ):
			return super().test_attention_slicing_forward_pass()







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




	"""simple docstring"""






	def    lowerCamelCase__					(     self   ):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Union[str, Any]     =   load_image(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
			    """repaint/celeba_hq_256.png"""   )
			_lowercase				: Optional[Any]     =   load_image(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""   )
			_lowercase				: Dict     =   load_numpy(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
			    """repaint/celeba_hq_256_result.npy"""   )

			_lowercase				: int     =   """google/ddpm-ema-celebahq-256"""
			_lowercase				: Optional[Any]     =   UNetaDModel.from_pretrained(UpperCAmelCase_   )
			_lowercase				: Union[str, Any]     =   RePaintScheduler.from_pretrained(UpperCAmelCase_   )

			_lowercase				: Optional[int]     =   RePaintPipeline(unet=UpperCAmelCase_				,scheduler=UpperCAmelCase_   ).to(UpperCAmelCase_   )
			repaint.set_progress_bar_config(disable=UpperCAmelCase_   )
			repaint.enable_attention_slicing()

			_lowercase				: Union[str, Any]     =   torch.manual_seed(0   )
			_lowercase				: List[Any]     =   repaint(
			    UpperCAmelCase_				,UpperCAmelCase_				,num_inference_steps=2_50				,eta=0.0				,jump_length=10				,jump_n_sample=10				,generator=UpperCAmelCase_				,output_type="""np"""				,)
			_lowercase				: Optional[Any]     =   output.images[0]

			assert image.shape == (2_56, 2_56, 3)
			assert np.abs(expected_image - image   ).mean() < 1E-2








"""simple docstring"""







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       ):




	"""simple docstring"""






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: Optional[int]     =   list(tokenizer_class.max_model_input_sizes.keys()   )
			for model_name in sa_models[:1]:
					_lowercase				: Any     =   tokenizer_class.from_pretrained(UpperCAmelCase_   )
					self.assertIsNotNone(UpperCAmelCase_   )
					self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )
					self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )

					for special_tok in tokenizer.all_special_tokens:
							self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )
							_lowercase				: Any     =   tokenizer.convert_tokens_to_ids(UpperCAmelCase_   )
							self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):
			_lowercase				: Any     =   pickle.dumps(UpperCAmelCase_   )
			self.assertIsNotNone(UpperCAmelCase_   )

			_lowercase				: Optional[int]     =   pickle.loads(UpperCAmelCase_   )

			# 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(UpperCAmelCase_				,UpperCAmelCase_   )   )
					else:
							self.assertEqual(UpperCAmelCase_				,UpperCAmelCase_   )






	@slow
	def    lowerCamelCase__					(     self   ):
			self.check_tokenizer_from_pretrained(UpperCAmelCase_   )






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






	@require_tokenizers
	def    lowerCamelCase__					(     self   ):
			import numpy as np

			_lowercase				: Optional[Any]     =   BertTokenizer.from_pretrained("""bert-base-cased"""   )
			_lowercase				: Dict     =   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    lowerCamelCase__					(     self   ):
			import tensorflow as tf

			def tf_array_equals(UpperCAmelCase_				,UpperCAmelCase_   ):
					return tf.reduce_all(tf.equal(UpperCAmelCase_				,UpperCAmelCase_   )   )

			_lowercase				: Optional[int]     =   BertTokenizer.from_pretrained("""bert-base-cased"""   )
			_lowercase				: Any     =   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   )				,UpperCAmelCase_   )

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






	@require_torch
	@require_tokenizers
	def    lowerCamelCase__					(     self   ):
			import torch

			_lowercase				: Union[str, Any]     =   BertTokenizer.from_pretrained("""bert-base-cased"""   )
			_lowercase				: int     =   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    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   BertTokenizer.from_pretrained("""bert-base-cased"""   )
			_lowercase				: Tuple     =   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    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[Any]     =   BertTokenizerFast.from_pretrained("""bert-base-cased"""   )
			_lowercase				: Optional[int]     =   tokenizer_r(["""Test""", """\xad""", """test"""]				,is_split_into_words=UpperCAmelCase_   )

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






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Tuple     =   BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}   )
			_lowercase				: Any     =   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:
					_lowercase				: Tuple     =   batch.convert_to_tensors(tensor_type="""np"""   )
			self.assertFalse(len(cs.err   )				,msg=f"""should have no warning, but got {cs.err}"""   )

			_lowercase				: List[str]     =   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}   )
			_lowercase				: Any     =   batch.convert_to_tensors(tensor_type="""np"""				,prepend_batch_axis=UpperCAmelCase_   )
			self.assertEqual(tensor_batch["""inputs"""].shape				,(1, 3)   )
			self.assertEqual(tensor_batch["""labels"""].shape				,(1,)   )






	@require_torch
	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}   )
			_lowercase				: Any     =   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:
					_lowercase				: str     =   batch.convert_to_tensors(tensor_type="""pt"""   )
			self.assertFalse(len(cs.err   )				,msg=f"""should have no warning, but got {cs.err}"""   )

			_lowercase				: Dict     =   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}   )
			_lowercase				: List[str]     =   batch.convert_to_tensors(tensor_type="""pt"""				,prepend_batch_axis=UpperCAmelCase_   )
			self.assertEqual(tensor_batch["""inputs"""].shape				,(1, 3)   )
			self.assertEqual(tensor_batch["""labels"""].shape				,(1,)   )






	@require_tf
	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}   )
			_lowercase				: List[str]     =   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:
					_lowercase				: Tuple     =   batch.convert_to_tensors(tensor_type="""tf"""   )
			self.assertFalse(len(cs.err   )				,msg=f"""should have no warning, but got {cs.err}"""   )

			_lowercase				: Tuple     =   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}   )
			_lowercase				: List[str]     =   batch.convert_to_tensors(tensor_type="""tf"""				,prepend_batch_axis=UpperCAmelCase_   )
			self.assertEqual(tensor_batch["""inputs"""].shape				,(1, 3)   )
			self.assertEqual(tensor_batch["""labels"""].shape				,(1,)   )






	@require_flax
	def    lowerCamelCase__					(     self   ):
			_lowercase				: Any     =   BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}   )
			_lowercase				: Tuple     =   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:
					_lowercase				: str     =   batch.convert_to_tensors(tensor_type="""jax"""   )
			self.assertFalse(len(cs.err   )				,msg=f"""should have no warning, but got {cs.err}"""   )

			_lowercase				: Union[str, Any]     =   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}   )
			_lowercase				: List[Any]     =   batch.convert_to_tensors(tensor_type="""jax"""				,prepend_batch_axis=UpperCAmelCase_   )
			self.assertEqual(tensor_batch["""inputs"""].shape				,(1, 3)   )
			self.assertEqual(tensor_batch["""labels"""].shape				,(1,)   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[int]     =   [{"""input_ids""": np.array([0, 1, 2]   )}, {"""input_ids""": np.array([0, 1, 2, 3]   )}]
			_lowercase				: Optional[int]     =   BertTokenizer.from_pretrained("""bert-base-cased"""   )

			_lowercase				: List[str]     =   tokenizer.pad(UpperCAmelCase_				,padding=UpperCAmelCase_   )
			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]]   )
			_lowercase				: Optional[int]     =   tokenizer.pad(UpperCAmelCase_				,padding=UpperCAmelCase_				,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    lowerCamelCase__					(     self   ):
			import torch

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

			_lowercase				: List[Any]     =   tokenizer.pad(UpperCAmelCase_				,padding=UpperCAmelCase_   )
			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]]   )
			_lowercase				: List[Any]     =   tokenizer.pad(UpperCAmelCase_				,padding=UpperCAmelCase_				,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    lowerCamelCase__					(     self   ):
			import tensorflow as tf

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

			_lowercase				: Optional[int]     =   tokenizer.pad(UpperCAmelCase_				,padding=UpperCAmelCase_   )
			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]]   )
			_lowercase				: str     =   tokenizer.pad(UpperCAmelCase_				,padding=UpperCAmelCase_				,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    lowerCamelCase__					(     self   ):
			_lowercase				: Tuple     =   Tokenizer(WordPiece(unk_token="""[UNK]"""   )   )
			PreTrainedTokenizerFast(tokenizer_object=UpperCAmelCase_   )






	@require_tokenizers
	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[Any]     =   Tokenizer(WordPiece(unk_token="""[UNK]"""   )   )
			with tempfile.TemporaryDirectory() as tmpdirname:
					bert_tokenizer.save(os.path.join(UpperCAmelCase_				,"""tokenizer.json"""   )   )
					PreTrainedTokenizerFast(tokenizer_file=os.path.join(UpperCAmelCase_				,"""tokenizer.json"""   )   )



