



"""simple docstring"""





import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__	,					SCREAMING_SNAKE_CASE__	,					unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase      	=							StableDiffusionInpaintPipeline
		_lowerCamelCase      	=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
		_lowerCamelCase      	=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		_lowerCamelCase      	=							frozenset(
		    []		)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
		_lowerCamelCase      	=							frozenset([]		)





		def  UpperCAmelCase__				(       self )			->    Dict:







								'''simple docstring'''

								torch.manual_seed(0 )
								snake_case_ :			Union[str, Any]            =      UNetaDConditionModel(
								    block_out_channels=(3_2, 6_4)				,     layers_per_block=2				,     sample_size=3_2				,     in_channels=9				,     out_channels=4				,     down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,     up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,     cross_attention_dim=3_2				,     attention_head_dim=(2, 4)				,     use_linear_projection=_lowercase				,     )
								snake_case_ :			Dict            =      PNDMScheduler(skip_prk_steps=_lowercase )
								torch.manual_seed(0 )
								snake_case_ :			str            =      AutoencoderKL(
								    block_out_channels=[3_2, 6_4]				,     in_channels=3				,     out_channels=3				,     down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,     up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,     latent_channels=4				,     sample_size=1_2_8				,     )
								torch.manual_seed(0 )
								snake_case_ :			Union[str, Any]            =      CLIPTextConfig(
								    bos_token_id=0				,     eos_token_id=2				,     hidden_size=3_2				,     intermediate_size=3_7				,     layer_norm_eps=1E-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_0_0_0				,     hidden_act="""gelu"""				,     projection_dim=5_1_2				,     )
								snake_case_ :			Dict            =      CLIPTextModel(_lowercase )
								snake_case_ :			Optional[Any]            =      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

								snake_case_ :			int            =      {
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """vae""": vae,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """safety_checker""": None,
								    """feature_extractor""": None,
								}
								return components





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase=0 )			->    Optional[Any]:







								'''simple docstring'''

								snake_case_ :			Tuple            =      floats_tensor((1, 3, 3_2, 3_2)				,     rng=random.Random(_lowercase ) ).to(_lowercase )
								snake_case_ :			str            =      image.cpu().permute(0				,     2				,     3				,     1 )[0]
								snake_case_ :			Tuple            =      Image.fromarray(np.uinta(_lowercase ) ).convert("""RGB""" ).resize((6_4, 6_4) )
								snake_case_ :			Any            =      Image.fromarray(np.uinta(image + 4 ) ).convert("""RGB""" ).resize((6_4, 6_4) )
								if str(_lowercase ).startswith("""mps""" ):
														snake_case_ :			str            =      torch.manual_seed(_lowercase )
								else:
														snake_case_ :			List[str]            =      torch.Generator(device=_lowercase ).manual_seed(_lowercase )
								snake_case_ :			Optional[Any]            =      {
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """image""": init_image,
								    """mask_image""": mask_image,
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								}
								return inputs





		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								snake_case_ :			Optional[Any]            =      """cpu"""  # ensure determinism for the device-dependent torch.Generator
								snake_case_ :			List[str]            =      self.get_dummy_components()
								snake_case_ :			Optional[Any]            =      StableDiffusionInpaintPipeline(**_lowercase )
								snake_case_ :			Dict            =      sd_pipe.to(_lowercase )
								sd_pipe.set_progress_bar_config(disable=_lowercase )

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

								assert image.shape == (1, 6_4, 6_4, 3)
								snake_case_ :			Optional[int]            =      np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476] )

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





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								super().test_inference_batch_single_identical(expected_max_diff=3E-3 )




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





		"""simple docstring"""






		def  UpperCAmelCase__				(       self )			->    str:







								'''simple docstring'''

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





		def  UpperCAmelCase__				(       self )			->    Any:







								'''simple docstring'''

								snake_case_ :			Union[str, Any]            =      load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png""" )
								snake_case_ :			List[Any]            =      load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
								snake_case_ :			Dict            =      load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
								    """/yellow_cat_sitting_on_a_park_bench.npy""" )

								snake_case_ :			str            =      """stabilityai/stable-diffusion-2-inpainting"""
								snake_case_ :			Optional[Any]            =      StableDiffusionInpaintPipeline.from_pretrained(_lowercase				,     safety_checker=_lowercase )
								pipe.to(_lowercase )
								pipe.set_progress_bar_config(disable=_lowercase )
								pipe.enable_attention_slicing()

								snake_case_ :			List[Any]            =      """Face of a yellow cat, high resolution, sitting on a park bench"""

								snake_case_ :			List[str]            =      torch.manual_seed(0 )
								snake_case_ :			Dict            =      pipe(
								    prompt=_lowercase				,     image=_lowercase				,     mask_image=_lowercase				,     generator=_lowercase				,     output_type="""np"""				,     )
								snake_case_ :			Tuple            =      output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image ).max() < 9E-3





		def  UpperCAmelCase__				(       self )			->    Optional[int]:







								'''simple docstring'''

								snake_case_ :			str            =      load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png""" )
								snake_case_ :			Tuple            =      load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
								snake_case_ :			Union[str, Any]            =      load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
								    """/yellow_cat_sitting_on_a_park_bench_fp16.npy""" )

								snake_case_ :			str            =      """stabilityai/stable-diffusion-2-inpainting"""
								snake_case_ :			Optional[Any]            =      StableDiffusionInpaintPipeline.from_pretrained(
								    _lowercase				,     torch_dtype=torch.floataa				,     safety_checker=_lowercase				,     )
								pipe.to(_lowercase )
								pipe.set_progress_bar_config(disable=_lowercase )
								pipe.enable_attention_slicing()

								snake_case_ :			Union[str, Any]            =      """Face of a yellow cat, high resolution, sitting on a park bench"""

								snake_case_ :			Optional[Any]            =      torch.manual_seed(0 )
								snake_case_ :			Any            =      pipe(
								    prompt=_lowercase				,     image=_lowercase				,     mask_image=_lowercase				,     generator=_lowercase				,     output_type="""np"""				,     )
								snake_case_ :			str            =      output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)
								assert np.abs(expected_image - image ).max() < 5E-1





		def  UpperCAmelCase__				(       self )			->    Optional[int]:







								'''simple docstring'''

								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								snake_case_ :			int            =      load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/sd2-inpaint/init_image.png""" )
								snake_case_ :			Optional[int]            =      load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )

								snake_case_ :			int            =      """stabilityai/stable-diffusion-2-inpainting"""
								snake_case_ :			List[str]            =      PNDMScheduler.from_pretrained(_lowercase				,     subfolder="""scheduler""" )
								snake_case_ :			Optional[Any]            =      StableDiffusionInpaintPipeline.from_pretrained(
								    _lowercase				,     safety_checker=_lowercase				,     scheduler=_lowercase				,     torch_dtype=torch.floataa				,     )
								pipe.to(_lowercase )
								pipe.set_progress_bar_config(disable=_lowercase )
								pipe.enable_attention_slicing(1 )
								pipe.enable_sequential_cpu_offload()

								snake_case_ :			Any            =      """Face of a yellow cat, high resolution, sitting on a park bench"""

								snake_case_ :			List[Any]            =      torch.manual_seed(0 )
								snake_case_ :			Any            =      pipe(
								    prompt=_lowercase				,     image=_lowercase				,     mask_image=_lowercase				,     generator=_lowercase				,     num_inference_steps=2				,     output_type="""np"""				,     )

								snake_case_ :			Dict            =      torch.cuda.max_memory_allocated()
								# make sure that less than 2.65 GB is allocated
								assert mem_bytes < 2.65 * 1_0**9



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase  =logging.get_logger(__name__)


lowerCamelCase  ="▁"

lowerCamelCase  ={"vocab_file": "spiece.model"}

lowerCamelCase  ={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

lowerCamelCase  ={
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_		 =	PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_		 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_		 =	['''input_ids''', '''attention_mask''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE="</s>"    ,    __SCREAMING_SNAKE_CASE="<unk>"    ,    __SCREAMING_SNAKE_CASE=[]    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           UpperCamelCase__     : int											=					{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=__SCREAMING_SNAKE_CASE    ,    unk_token=__SCREAMING_SNAKE_CASE    ,    additional_special_tokens=__SCREAMING_SNAKE_CASE    ,    sp_model_kwargs=self.sp_model_kwargs    ,    **__SCREAMING_SNAKE_CASE    ,    )

           UpperCamelCase__     : Dict											=					vocab_file
           UpperCamelCase__     : Any											=					spm.SentencePieceProcessor(**self.sp_model_kwargs	)
           self.sp_model.Load(__SCREAMING_SNAKE_CASE	)






     @property
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           return self.sp_model.get_piece_size()






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict[str, int]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE	): i for i in range(self.vocab_size	)}
           vocab.update(self.added_tokens_encoder	)
           return vocab






     def __getstate__(				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					self.__dict__.copy()
           UpperCamelCase__     : int											=					None
           return state






     def __setstate__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : int											=					d

           # for backward compatibility
           if not hasattr(self    ,    '''sp_model_kwargs'''	):
                 UpperCamelCase__     : int											=					{}

           UpperCamelCase__     : Optional[Any]											=					spm.SentencePieceProcessor(**self.sp_model_kwargs	)
           self.sp_model.Load(self.vocab_file	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> List[str]:




           """simple docstring"""







           return self.sp_model.encode(__SCREAMING_SNAKE_CASE    ,    out_type=__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> List[str]:




           """simple docstring"""







           return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> List[Any]:




           """simple docstring"""







           if index < self.sp_model.get_piece_size():
                 UpperCamelCase__     : str											=					self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE	)
           return token






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					[]
           UpperCamelCase__     : Union[str, Any]											=					''''''
           for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                       out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE	) + token
                       UpperCamelCase__     : str											=					[]
                 else:
                       current_sub_tokens.append(__SCREAMING_SNAKE_CASE	)
           out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE	)
           return out_string.strip()






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None	)	-> Tuple[str]:




           """simple docstring"""







           if not os.path.isdir(__SCREAMING_SNAKE_CASE	):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
                 return
           UpperCamelCase__     : Optional[int]											=					os.path.join(
               __SCREAMING_SNAKE_CASE    ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

           if os.path.abspath(self.vocab_file	) != os.path.abspath(__SCREAMING_SNAKE_CASE	) and os.path.isfile(self.vocab_file	):
                 copyfile(self.vocab_file    ,    __SCREAMING_SNAKE_CASE	)
           elif not os.path.isfile(self.vocab_file	):
                 with open(__SCREAMING_SNAKE_CASE    ,    '''wb'''	) as fi:
                       UpperCamelCase__     : int											=					self.sp_model.serialized_model_proto()
                       fi.write(__SCREAMING_SNAKE_CASE	)

           return (out_vocab_file,)


