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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase			:							Tuple     =logging.get_logger(__name__)

lowerCAmelCase			:							int     ="▁"

lowerCAmelCase			:							Tuple     ={"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}

lowerCAmelCase			:							str     ={
    "vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model",
    },
    "monolingual_vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt",
    },
}

lowerCAmelCase			:							Any     ={"vinai/bartpho-syllable": 1024}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






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



      def __init__(					self     :				str    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				Any="<s>"    ,  _UpperCamelCase     :				List[str]="</s>"    ,  _UpperCamelCase     :				Any="</s>"    ,  _UpperCamelCase     :				Tuple="<s>"    ,  _UpperCamelCase     :				List[str]="<unk>"    ,  _UpperCamelCase     :				Tuple="<pad>"    ,  _UpperCamelCase     :				Any="<mask>"    ,  _UpperCamelCase     :				Optional[Dict[str, Any]] = None    ,  **_UpperCamelCase     :				Optional[int]    ,  )				->None:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						AddedToken(_UpperCamelCase    ,  lstrip=_UpperCamelCase    ,  rstrip=_UpperCamelCase) if isinstance(_UpperCamelCase    ,  _UpperCamelCase) else mask_token

          _lowerCamelCase     :		Union[str, Any] 						=						{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              bos_token=_UpperCamelCase    ,  eos_token=_UpperCamelCase    ,  unk_token=_UpperCamelCase    ,  sep_token=_UpperCamelCase    ,  cls_token=_UpperCamelCase    ,  pad_token=_UpperCamelCase    ,  mask_token=_UpperCamelCase    ,  sp_model_kwargs=self.sp_model_kwargs    ,  **_UpperCamelCase    ,  )

          _lowerCamelCase     :		Union[str, Any] 						=						vocab_file
          _lowerCamelCase     :		Optional[int] 						=						monolingual_vocab_file
          _lowerCamelCase     :		Optional[Any] 						=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(str(_UpperCamelCase))

          # Load the reduced vocab

          # Keep order of special tokens for backward compatibility
          _lowerCamelCase     :		str 						=						{}
          _lowerCamelCase     :		str 						=						0
          for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
              if str(_UpperCamelCase) not in self.fairseq_tokens_to_ids:
                  _lowerCamelCase     :		Union[str, Any] 						=						cnt
                  cnt += 1
          with open(_UpperCamelCase    ,  """r"""    ,  encoding="""utf-8""") as f:
              for line in f.readlines():
                  _lowerCamelCase     :		Union[str, Any] 						=						line.strip().split()[0]
                  _lowerCamelCase     :		Union[str, Any] 						=						len(self.fairseq_tokens_to_ids)
          if str(_UpperCamelCase) not in self.fairseq_tokens_to_ids:
              _lowerCamelCase     :		List[str] 						=						len(self.fairseq_tokens_to_ids)

          _lowerCamelCase     :		List[str] 						=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}



      def __getstate__(					self     :				Union[str, Any])				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						self.__dict__.copy()
          _lowerCamelCase     :		Any 						=						None
          _lowerCamelCase     :		str 						=						self.sp_model.serialized_model_proto()
          return state



      def __setstate__(					self     :				Union[str, Any]    ,  _UpperCamelCase     :				Dict)				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						d

          # for backward compatibility
          if not hasattr(self    ,  """sp_model_kwargs"""):
              _lowerCamelCase     :		Optional[Any] 						=						{}

          _lowerCamelCase     :		Union[str, Any] 						=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				List[int]    ,  _UpperCamelCase     :				Optional[List[int]] = None)				->List[int]:


          """simple docstring"""


          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          _lowerCamelCase     :		Dict 						=						[self.cls_token_id]
          _lowerCamelCase     :		List[Any] 						=						[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				List[int]    ,  _UpperCamelCase     :				Optional[List[int]] = None    ,  _UpperCamelCase     :				bool = False)				->List[int]:


          """simple docstring"""


          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=_UpperCamelCase    ,  token_ids_a=_UpperCamelCase    ,  already_has_special_tokens=_UpperCamelCase)

          if token_ids_a is None:
              return [1] + ([0] * len(_UpperCamelCase)) + [1]
          return [1] + ([0] * len(_UpperCamelCase)) + [1, 1] + ([0] * len(_UpperCamelCase)) + [1]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				List[int]    ,  _UpperCamelCase     :				Optional[List[int]] = None)				->List[int]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						[self.sep_token_id]
          _lowerCamelCase     :		List[Any] 						=						[self.cls_token_id]

          if token_ids_a is None:
              return len(cls + token_ids_a + sep) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Tuple:


          """simple docstring"""


          return len(self.fairseq_ids_to_tokens)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->int:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						{self.convert_ids_to_tokens(_UpperCamelCase): i for i in range(self.vocab_size)}
          vocab.update(self.added_tokens_encoder)
          return vocab



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				str)				->List[str]:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				Tuple)				->List[Any]:


          """simple docstring"""


          if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
          else:
              return self.unk_token_id



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				Union[str, Any])				->List[str]:


          """simple docstring"""


          return self.fairseq_ids_to_tokens[index]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				List[Any])				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						"""""".join(_UpperCamelCase).replace(_UpperCamelCase    ,  """ """).strip()
          return out_string




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[str] = None)				->Tuple[str]:


          """simple docstring"""


          if not os.path.isdir(_UpperCamelCase):
              logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
              return
          _lowerCamelCase     :		Dict 						=						os.path.join(
              _UpperCamelCase    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
          _lowerCamelCase     :		str 						=						os.path.join(
              _UpperCamelCase    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""]    ,  )

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

          if os.path.abspath(self.monolingual_vocab_file) != os.path.abspath(
              _UpperCamelCase) and os.path.isfile(self.monolingual_vocab_file):
              copyfile(self.monolingual_vocab_file    ,  _UpperCamelCase)
          elif not os.path.isfile(self.monolingual_vocab_file):
              with open(_UpperCamelCase    ,  """w"""    ,  encoding="""utf-8""") as fp:
                  for token in self.fairseq_tokens_to_ids:
                      if token not in self.all_special_tokens:
                          fp.write(F"""{str(_UpperCamelCase)} \n""")

          return out_vocab_file, out_monolingual_vocab_file





import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin




@skip_mps
class 	__snake_case	( __lowerCAmelCase ,  __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       IFPipeline
      _snake_case    		=       TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
      _snake_case    		=       TEXT_TO_IMAGE_BATCH_PARAMS
      _snake_case    		=       PipelineTesterMixin.required_optional_params - {'latents'}



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Optional[Any]:


          """simple docstring"""


          return self._get_dummy_components()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				List[Any]=0)				->Optional[Any]:


          """simple docstring"""


          if str(_UpperCamelCase).startswith("""mps"""):
              _lowerCamelCase     :		int 						=						torch.manual_seed(_UpperCamelCase)
          else:
              _lowerCamelCase     :		List[Any] 						=						torch.Generator(device=_UpperCamelCase).manual_seed(_UpperCamelCase)

          _lowerCamelCase     :		Dict 						=						{
              """prompt""": """A painting of a squirrel eating a burger""",
              """generator""": generator,
              """num_inference_steps""": 2,
              """output_type""": """numpy""",
          }

          return inputs



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Union[str, Any]:


          """simple docstring"""


          self._test_save_load_optional_components()



      @unittest.skipIf(torch_device != """cuda"""    ,  reason="""float16 requires CUDA""")
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


          super().test_save_load_floataa(expected_max_diff=1E-1)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Any:


          """simple docstring"""


          self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Union[str, Any]:


          """simple docstring"""


          self._test_save_load_local()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->Dict:


          """simple docstring"""


          self._test_inference_batch_single_identical(
              expected_max_diff=1E-2    ,  )




      @unittest.skipIf(
          torch_device != """cuda""" or not is_xformers_available()    ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,  )
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->int:


          """simple docstring"""


          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)





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





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[str]:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""    ,  variant="""fp16"""    ,  torch_dtype=torch.floataa)

          _lowerCamelCase     :		Tuple 						=						IFSuperResolutionPipeline.from_pretrained(
              """DeepFloyd/IF-II-L-v1.0"""    ,  variant="""fp16"""    ,  torch_dtype=torch.floataa    ,  text_encoder=_UpperCamelCase    ,  tokenizer=_UpperCamelCase)

          # pre compute text embeddings and remove T5 to save memory

          pipe_a.text_encoder.to("""cuda""")

          _lowerCamelCase						,							_lowerCamelCase     :		str 						=						pipe_a.encode_prompt("""anime turtle"""    ,  device="""cuda""")

          del pipe_a.tokenizer
          del pipe_a.text_encoder
          gc.collect()

          _lowerCamelCase     :		str 						=						None
          _lowerCamelCase     :		str 						=						None

          pipe_a.enable_model_cpu_offload()
          pipe_a.enable_model_cpu_offload()

          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

          self._test_if(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # img2img

          _lowerCamelCase     :		Optional[Any] 						=						IFImgaImgPipeline(**pipe_a.components)
          _lowerCamelCase     :		Optional[Any] 						=						IFImgaImgSuperResolutionPipeline(**pipe_a.components)

          pipe_a.enable_model_cpu_offload()
          pipe_a.enable_model_cpu_offload()

          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

          self._test_if_imgaimg(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)

          pipe_a.remove_all_hooks()
          pipe_a.remove_all_hooks()

          # inpainting

          _lowerCamelCase     :		Any 						=						IFInpaintingPipeline(**pipe_a.components)
          _lowerCamelCase     :		Dict 						=						IFInpaintingSuperResolutionPipeline(**pipe_a.components)

          pipe_a.enable_model_cpu_offload()
          pipe_a.enable_model_cpu_offload()

          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
          pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

          self._test_if_inpainting(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				str)				->Tuple:


          """simple docstring"""


          _start_torch_memory_measurement()

          _lowerCamelCase     :		Optional[int] 						=						torch.Generator(device="""cpu""").manual_seed(0)
          _lowerCamelCase     :		Optional[Any] 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  num_inference_steps=2    ,  generator=_UpperCamelCase    ,  output_type="""np"""    ,  )

          _lowerCamelCase     :		Optional[int] 						=						output.images[0]

          assert image.shape == (64, 64, 3)

          _lowerCamelCase     :		Dict 						=						torch.cuda.max_memory_allocated()
          assert mem_bytes < 13 * 10**9

          _lowerCamelCase     :		Dict 						=						load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy""")
          assert_mean_pixel_difference(_UpperCamelCase    ,  _UpperCamelCase)

          # pipeline 2

          _start_torch_memory_measurement()

          _lowerCamelCase     :		Tuple 						=						torch.Generator(device="""cpu""").manual_seed(0)

          _lowerCamelCase     :		int 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(0)).to(_UpperCamelCase)

          _lowerCamelCase     :		str 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  image=_UpperCamelCase    ,  generator=_UpperCamelCase    ,  num_inference_steps=2    ,  output_type="""np"""    ,  )

          _lowerCamelCase     :		Any 						=						output.images[0]

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

          _lowerCamelCase     :		Tuple 						=						torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          _lowerCamelCase     :		int 						=						load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy""")
          assert_mean_pixel_difference(_UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				List[Any])				->Any:


          """simple docstring"""


          _start_torch_memory_measurement()

          _lowerCamelCase     :		int 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(0)).to(_UpperCamelCase)

          _lowerCamelCase     :		Union[str, Any] 						=						torch.Generator(device="""cpu""").manual_seed(0)

          _lowerCamelCase     :		Dict 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  image=_UpperCamelCase    ,  num_inference_steps=2    ,  generator=_UpperCamelCase    ,  output_type="""np"""    ,  )

          _lowerCamelCase     :		Union[str, Any] 						=						output.images[0]

          assert image.shape == (64, 64, 3)

          _lowerCamelCase     :		Optional[Any] 						=						torch.cuda.max_memory_allocated()
          assert mem_bytes < 10 * 10**9

          _lowerCamelCase     :		List[Any] 						=						load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy""")
          assert_mean_pixel_difference(_UpperCamelCase    ,  _UpperCamelCase)

          # pipeline 2

          _start_torch_memory_measurement()

          _lowerCamelCase     :		Tuple 						=						torch.Generator(device="""cpu""").manual_seed(0)

          _lowerCamelCase     :		List[str] 						=						floats_tensor((1, 3, 256, 256)    ,  rng=random.Random(0)).to(_UpperCamelCase)
          _lowerCamelCase     :		List[Any] 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(0)).to(_UpperCamelCase)

          _lowerCamelCase     :		Optional[Any] 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  image=_UpperCamelCase    ,  original_image=_UpperCamelCase    ,  generator=_UpperCamelCase    ,  num_inference_steps=2    ,  output_type="""np"""    ,  )

          _lowerCamelCase     :		List[Any] 						=						output.images[0]

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

          _lowerCamelCase     :		str 						=						torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          _lowerCamelCase     :		int 						=						load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy""")
          assert_mean_pixel_difference(_UpperCamelCase    ,  _UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				Tuple)				->Optional[int]:


          """simple docstring"""


          _start_torch_memory_measurement()

          _lowerCamelCase     :		Tuple 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(0)).to(_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(1)).to(_UpperCamelCase)

          _lowerCamelCase     :		int 						=						torch.Generator(device="""cpu""").manual_seed(0)
          _lowerCamelCase     :		Any 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  image=_UpperCamelCase    ,  mask_image=_UpperCamelCase    ,  num_inference_steps=2    ,  generator=_UpperCamelCase    ,  output_type="""np"""    ,  )

          _lowerCamelCase     :		Any 						=						output.images[0]

          assert image.shape == (64, 64, 3)

          _lowerCamelCase     :		List[Any] 						=						torch.cuda.max_memory_allocated()
          assert mem_bytes < 10 * 10**9

          _lowerCamelCase     :		str 						=						load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy""")
          assert_mean_pixel_difference(_UpperCamelCase    ,  _UpperCamelCase)

          # pipeline 2

          _start_torch_memory_measurement()

          _lowerCamelCase     :		Tuple 						=						torch.Generator(device="""cpu""").manual_seed(0)

          _lowerCamelCase     :		Union[str, Any] 						=						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(0)).to(_UpperCamelCase)
          _lowerCamelCase     :		Union[str, Any] 						=						floats_tensor((1, 3, 256, 256)    ,  rng=random.Random(0)).to(_UpperCamelCase)
          _lowerCamelCase     :		Optional[int] 						=						floats_tensor((1, 3, 256, 256)    ,  rng=random.Random(1)).to(_UpperCamelCase)

          _lowerCamelCase     :		List[str] 						=						pipe_a(
              prompt_embeds=_UpperCamelCase    ,  negative_prompt_embeds=_UpperCamelCase    ,  image=_UpperCamelCase    ,  mask_image=_UpperCamelCase    ,  original_image=_UpperCamelCase    ,  generator=_UpperCamelCase    ,  num_inference_steps=2    ,  output_type="""np"""    ,  )

          _lowerCamelCase     :		Optional[Any] 						=						output.images[0]

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

          _lowerCamelCase     :		Optional[Any] 						=						torch.cuda.max_memory_allocated()
          assert mem_bytes < 4 * 10**9

          _lowerCamelCase     :		int 						=						load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy""")
          assert_mean_pixel_difference(_UpperCamelCase    ,  _UpperCamelCase)







def 						A__  (					):







    '''simple docstring'''
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()




