import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
      import torch


if is_vision_available():
      from PIL import Image

      from transformers import MgpstrProcessor, ViTImageProcessor




@require_torch
@require_vision
class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       ViTImageProcessor if is_vision_available() else None



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Dict:


          """simple docstring"""


          return self.image_processor_tester.prepare_image_processor_dict()



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


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						(3, 32, 128)
          _lowerCamelCase     :		str 						=						tempfile.mkdtemp()

          # fmt: off
          _lowerCamelCase     :		Dict 						=						["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
          # fmt: on
          _lowerCamelCase     :		str 						=						dict(zip(_UpperCamelCase    ,  range(len(_UpperCamelCase))))

          _lowerCamelCase     :		int 						=						os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES["""vocab_file"""])
          with open(self.vocab_file    ,  """w"""    ,  encoding="""utf-8""") as fp:
              fp.write(json.dumps(_UpperCamelCase) + """\n""")

          _lowerCamelCase     :		Any 						=						{
              """do_normalize""": False,
              """do_resize""": True,
              """image_processor_type""": """ViTImageProcessor""",
              """resample""": 3,
              """size""": {"""height""": 32, """width""": 128},
          }
          _lowerCamelCase     :		Union[str, Any] 						=						os.path.join(self.tmpdirname    ,  _UpperCamelCase)
          with open(self.image_processor_file    ,  """w"""    ,  encoding="""utf-8""") as fp:
              json.dump(_UpperCamelCase    ,  _UpperCamelCase)



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


          """simple docstring"""


          return MgpstrTokenizer.from_pretrained(self.tmpdirname    ,  **_UpperCamelCase)



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


          """simple docstring"""


          return ViTImageProcessor.from_pretrained(self.tmpdirname    ,  **_UpperCamelCase)



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


          """simple docstring"""


          shutil.rmtree(self.tmpdirname)



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


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						np.random.randint(255    ,  size=(3, 30, 400)    ,  dtype=np.uinta)

          _lowerCamelCase     :		int 						=						Image.fromarray(np.moveaxis(_UpperCamelCase    ,  0    ,  -1))

          return image_input



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.get_tokenizer()
          _lowerCamelCase     :		Tuple 						=						self.get_image_processor()

          _lowerCamelCase     :		Union[str, Any] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)
          processor.save_pretrained(self.tmpdirname)
          _lowerCamelCase     :		int 						=						MgpstrProcessor.from_pretrained(self.tmpdirname    ,  use_fast=_UpperCamelCase)

          self.assertEqual(processor.char_tokenizer.get_vocab()    ,  tokenizer.get_vocab())
          self.assertIsInstance(processor.char_tokenizer    ,  _UpperCamelCase)

          self.assertEqual(processor.image_processor.to_json_string()    ,  image_processor.to_json_string())
          self.assertIsInstance(processor.image_processor    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.get_tokenizer()
          _lowerCamelCase     :		Optional[Any] 						=						self.get_image_processor()

          _lowerCamelCase     :		Any 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)
          processor.save_pretrained(self.tmpdirname)

          _lowerCamelCase     :		Tuple 						=						self.get_tokenizer(bos_token="""(BOS)"""    ,  eos_token="""(EOS)""")
          _lowerCamelCase     :		Union[str, Any] 						=						self.get_image_processor(do_normalize=_UpperCamelCase    ,  padding_value=1.0)

          _lowerCamelCase     :		Tuple 						=						MgpstrProcessor.from_pretrained(
              self.tmpdirname    ,  bos_token="""(BOS)"""    ,  eos_token="""(EOS)"""    ,  do_normalize=_UpperCamelCase    ,  padding_value=1.0)

          self.assertEqual(processor.char_tokenizer.get_vocab()    ,  tokenizer_add_kwargs.get_vocab())
          self.assertIsInstance(processor.char_tokenizer    ,  _UpperCamelCase)

          self.assertEqual(processor.image_processor.to_json_string()    ,  image_processor_add_kwargs.to_json_string())
          self.assertIsInstance(processor.image_processor    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->int:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		List[str] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		List[str] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		Optional[int] 						=						image_processor(_UpperCamelCase    ,  return_tensors="""np""")
          _lowerCamelCase     :		int 						=						processor(images=_UpperCamelCase    ,  return_tensors="""np""")

          for key in input_image_proc.keys():
              self.assertAlmostEqual(input_image_proc[key].sum()    ,  input_processor[key].sum()    ,  delta=1E-2)



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


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		Any 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Optional[int] 						=						"""test"""

          _lowerCamelCase     :		Union[str, Any] 						=						processor(text=_UpperCamelCase)

          _lowerCamelCase     :		Dict 						=						tokenizer(_UpperCamelCase)
          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]    ,  encoded_processor[key])



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


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		List[Any] 						=						self.get_tokenizer()

          _lowerCamelCase     :		Any 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Any 						=						"""test"""
          _lowerCamelCase     :		List[str] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		int 						=						processor(text=_UpperCamelCase    ,  images=_UpperCamelCase)

          self.assertListEqual(list(inputs.keys())    ,  ["""pixel_values""", """labels"""])

          # test if it raises when no input is passed
          with pytest.raises(_UpperCamelCase):
              processor()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		List[str] 						=						self.get_tokenizer()

          _lowerCamelCase     :		Dict 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		int 						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

          _lowerCamelCase     :		Any 						=						processor.char_decode(_UpperCamelCase)
          _lowerCamelCase     :		Tuple 						=						tokenizer.batch_decode(_UpperCamelCase)
          _lowerCamelCase     :		List[str] 						=						[seq.replace(""" """    ,  """""") for seq in decoded_tok]

          self.assertListEqual(_UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->str:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.get_image_processor()
          _lowerCamelCase     :		str 						=						self.get_tokenizer()

          _lowerCamelCase     :		List[Any] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		int 						=						None
          _lowerCamelCase     :		Union[str, Any] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		Union[str, Any] 						=						processor(text=_UpperCamelCase    ,  images=_UpperCamelCase)

          self.assertListEqual(list(inputs.keys())    ,  processor.model_input_names)




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


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		Union[str, Any] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Any 						=						torch.randn(1    ,  27    ,  38)
          _lowerCamelCase     :		List[Any] 						=						torch.randn(1    ,  27    ,  5_0257)
          _lowerCamelCase     :		List[str] 						=						torch.randn(1    ,  27    ,  3_0522)

          _lowerCamelCase     :		int 						=						processor.batch_decode([char_input, bpe_input, wp_input])

          self.assertListEqual(list(results.keys())    ,  ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""])





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()




