

"""simple docstring"""






import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
 from test_module.custom_tokenization_fast import CustomTokenizerFast
class        _lowercase					(     unittest.TestCase     ):







     def 	lowerCAmelCase__    (      self       ):
      # A mock response for an HTTP head request to emulate server down
      __magic_name__     =      mock.Mock()
      __magic_name__     =      500
      __magic_name__     =      {}
      __magic_name__     =      HTTPError
      __magic_name__     =      {}

      # Download this model to make sure it's in the cache.
      __magic_name__     =      BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''       )

      # Under the mock environment we get a 500 error when trying to reach the tokenizer.
      with mock.patch('''requests.Session.request''' ,						return_value=UpperCamelCase_       ) as mock_head:
       __magic_name__     =      BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-bert'''       )
       # This check we did call the fake head request
       mock_head.assert_called()







     @require_tokenizers
     def 	lowerCAmelCase__    (      self       ):
      # A mock response for an HTTP head request to emulate server down
      __magic_name__     =      mock.Mock()
      __magic_name__     =      500
      __magic_name__     =      {}
      __magic_name__     =      HTTPError
      __magic_name__     =      {}

      # Download this model to make sure it's in the cache.
      __magic_name__     =      GPTaTokenizerFast.from_pretrained('''gpt2'''       )

      # Under the mock environment we get a 500 error when trying to reach the tokenizer.
      with mock.patch('''requests.Session.request''' ,						return_value=UpperCamelCase_       ) as mock_head:
       __magic_name__     =      GPTaTokenizerFast.from_pretrained('''gpt2'''       )
       # This check we did call the fake head request
       mock_head.assert_called()







     def 	lowerCAmelCase__    (      self       ):
      # This test is for deprecated behavior and can be removed in v5
      try:
       __magic_name__     =      tempfile.mktemp()
       with open(UpperCamelCase_ ,						'''wb'''       ) as f:
        http_get('''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''' ,						UpperCamelCase_       )

       __magic_name__     =      AlbertTokenizer.from_pretrained(UpperCamelCase_       )
      finally:
       os.remove(UpperCamelCase_       )

      # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
      # the current folder and have the right name.
      if os.path.isfile('''tokenizer.json'''       ):
       # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
       return
      try:
       with open('''tokenizer.json''' ,						'''wb'''       ) as f:
        http_get('''https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json''' ,						UpperCamelCase_       )
       __magic_name__     =      AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''       )
       # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
       self.assertEqual(tokenizer.vocab_size ,						1000       )
       # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

      finally:
       os.remove('''tokenizer.json'''       )







     def 	lowerCAmelCase__    (      self       ):
      # This test is for deprecated behavior and can be removed in v5
      __magic_name__     =      AlbertTokenizer.from_pretrained('''https://huggingface.co/albert-base-v1/resolve/main/spiece.model'''       )


@is_staging_test
class        _lowercase					(     unittest.TestCase     ):
     _lowerCamelCase     =       ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']







     @classmethod
     def 	lowerCAmelCase__    (      cls       ):
      __magic_name__     =      TOKEN
      HfFolder.save_token(UpperCamelCase_       )







     @classmethod
     def 	lowerCAmelCase__    (      cls       ):
      try:
       delete_repo(token=cls._token ,						repo_id='''test-tokenizer'''       )
      except HTTPError:
       pass

      try:
       delete_repo(token=cls._token ,						repo_id='''valid_org/test-tokenizer-org'''       )
      except HTTPError:
       pass

      try:
       delete_repo(token=cls._token ,						repo_id='''test-dynamic-tokenizer'''       )
      except HTTPError:
       pass







     def 	lowerCAmelCase__    (      self       ):
      with tempfile.TemporaryDirectory() as tmp_dir:
       __magic_name__     =      os.path.join(UpperCamelCase_ ,						'''vocab.txt'''       )
       with open(UpperCamelCase_ ,						'''w''' ,						encoding='''utf-8'''       ) as vocab_writer:
        vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]       )       )
       __magic_name__     =      BertTokenizer(UpperCamelCase_       )

      tokenizer.push_to_hub('''test-tokenizer''' ,						use_auth_token=self._token       )
      __magic_name__     =      BertTokenizer.from_pretrained(f'''{USER}/test-tokenizer'''       )
      self.assertDictEqual(new_tokenizer.vocab ,						tokenizer.vocab       )

      # Reset repo
      delete_repo(token=self._token ,						repo_id='''test-tokenizer'''       )

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
       tokenizer.save_pretrained(UpperCamelCase_ ,						repo_id='''test-tokenizer''' ,						push_to_hub=UpperCamelCase_ ,						use_auth_token=self._token       )

      __magic_name__     =      BertTokenizer.from_pretrained(f'''{USER}/test-tokenizer'''       )
      self.assertDictEqual(new_tokenizer.vocab ,						tokenizer.vocab       )







     def 	lowerCAmelCase__    (      self       ):
      with tempfile.TemporaryDirectory() as tmp_dir:
       __magic_name__     =      os.path.join(UpperCamelCase_ ,						'''vocab.txt'''       )
       with open(UpperCamelCase_ ,						'''w''' ,						encoding='''utf-8'''       ) as vocab_writer:
        vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]       )       )
       __magic_name__     =      BertTokenizer(UpperCamelCase_       )

      tokenizer.push_to_hub('''valid_org/test-tokenizer-org''' ,						use_auth_token=self._token       )
      __magic_name__     =      BertTokenizer.from_pretrained('''valid_org/test-tokenizer-org'''       )
      self.assertDictEqual(new_tokenizer.vocab ,						tokenizer.vocab       )

      # Reset repo
      delete_repo(token=self._token ,						repo_id='''valid_org/test-tokenizer-org'''       )

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
       tokenizer.save_pretrained(
           UpperCamelCase_ ,						repo_id='''valid_org/test-tokenizer-org''' ,						push_to_hub=UpperCamelCase_ ,						use_auth_token=self._token       )

      __magic_name__     =      BertTokenizer.from_pretrained('''valid_org/test-tokenizer-org'''       )
      self.assertDictEqual(new_tokenizer.vocab ,						tokenizer.vocab       )







     @require_tokenizers
     def 	lowerCAmelCase__    (      self       ):
      CustomTokenizer.register_for_auto_class()
      with tempfile.TemporaryDirectory() as tmp_dir:
       __magic_name__     =      os.path.join(UpperCamelCase_ ,						'''vocab.txt'''       )
       with open(UpperCamelCase_ ,						'''w''' ,						encoding='''utf-8'''       ) as vocab_writer:
        vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]       )       )
       __magic_name__     =      CustomTokenizer(UpperCamelCase_       )

      # No fast custom tokenizer
      tokenizer.push_to_hub('''test-dynamic-tokenizer''' ,						use_auth_token=self._token       )

      __magic_name__     =      AutoTokenizer.from_pretrained(f'''{USER}/test-dynamic-tokenizer''' ,						trust_remote_code=UpperCamelCase_       )
      # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
      self.assertEqual(tokenizer.__class__.__name__ ,						'''CustomTokenizer'''       )

      # Fast and slow custom tokenizer
      CustomTokenizerFast.register_for_auto_class()
      with tempfile.TemporaryDirectory() as tmp_dir:
       __magic_name__     =      os.path.join(UpperCamelCase_ ,						'''vocab.txt'''       )
       with open(UpperCamelCase_ ,						'''w''' ,						encoding='''utf-8'''       ) as vocab_writer:
        vocab_writer.write(''''''.join([x + '''\n''' for x in self.vocab_tokens]       )       )

       __magic_name__     =      BertTokenizerFast.from_pretrained(UpperCamelCase_       )
       bert_tokenizer.save_pretrained(UpperCamelCase_       )
       __magic_name__     =      CustomTokenizerFast.from_pretrained(UpperCamelCase_       )

      tokenizer.push_to_hub('''test-dynamic-tokenizer''' ,						use_auth_token=self._token       )

      __magic_name__     =      AutoTokenizer.from_pretrained(f'''{USER}/test-dynamic-tokenizer''' ,						trust_remote_code=UpperCamelCase_       )
      # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
      self.assertEqual(tokenizer.__class__.__name__ ,						'''CustomTokenizerFast'''       )
      __magic_name__     =      AutoTokenizer.from_pretrained(
          f'''{USER}/test-dynamic-tokenizer''' ,						use_fast=UpperCamelCase_ ,						trust_remote_code=UpperCamelCase_       )
      # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
      self.assertEqual(tokenizer.__class__.__name__ ,						'''CustomTokenizer'''       )


class        _lowercase					(     unittest.TestCase     ):







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      Trie()
      trie.add('''Hello 友達'''       )
      self.assertEqual(trie.data ,						{'''H''': {'''e''': {'''l''': {'''l''': {'''o''': {''' ''': {'''友''': {'''達''': {'''''': 1}}}}}}}}}       )
      trie.add('''Hello'''       )
      trie.data
      self.assertEqual(trie.data ,						{'''H''': {'''e''': {'''l''': {'''l''': {'''o''': {'''''': 1, ''' ''': {'''友''': {'''達''': {'''''': 1}}}}}}}}}       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      Trie()
      self.assertEqual(trie.split('''[CLS] This is a extra_id_100'''       ) ,						['''[CLS] This is a extra_id_100''']       )
      trie.add('''[CLS]'''       )
      trie.add('''extra_id_1'''       )
      trie.add('''extra_id_100'''       )
      self.assertEqual(trie.split('''[CLS] This is a extra_id_100'''       ) ,						['''[CLS]''', ''' This is a ''', '''extra_id_100''']       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      Trie()
      trie.add('''A'''       )
      self.assertEqual(trie.split('''ABC'''       ) ,						['''A''', '''BC''']       )
      self.assertEqual(trie.split('''BCA'''       ) ,						['''BC''', '''A''']       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      Trie()
      trie.add('''TOKEN]'''       )
      trie.add('''[SPECIAL_TOKEN]'''       )
      self.assertEqual(trie.split('''This is something [SPECIAL_TOKEN]'''       ) ,						['''This is something ''', '''[SPECIAL_TOKEN]''']       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      Trie()
      trie.add('''A'''       )
      trie.add('''P'''       )
      trie.add('''[SPECIAL_TOKEN]'''       )
      self.assertEqual(trie.split('''This is something [SPECIAL_TOKEN]'''       ) ,						['''This is something ''', '''[SPECIAL_TOKEN]''']       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      Trie()
      trie.add('''AB'''       )
      trie.add('''B'''       )
      trie.add('''C'''       )
      self.assertEqual(trie.split('''ABC'''       ) ,						['''AB''', '''C''']       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      Trie()
      trie.add('''ABC'''       )
      trie.add('''B'''       )
      trie.add('''CD'''       )
      self.assertEqual(trie.split('''ABCD'''       ) ,						['''ABC''', '''D''']       )







     def 	lowerCAmelCase__    (      self       ):
      # Even if the offsets are wrong, we necessarily output correct string
      # parts.
      __magic_name__     =      Trie()
      __magic_name__     =      trie.cut_text('''ABC''' ,						[0, 0, 2, 1, 2, 3]       )
      self.assertEqual(UpperCamelCase_ ,						['''AB''', '''C''']       )




"""simple docstring"""






import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





def 	lowercase      (   __UpperCamelCase						) ->      Union[str, Any]:
 __magic_name__     =      [tensor.shape for tensor in tensor_list]
 return all(shape == shapes[0] for shape in shapes[1:]						)
class        _lowercase					(     __UpperCAmelCase   , __UpperCAmelCase   , __UpperCAmelCase   , unittest.TestCase     ):
     _lowerCamelCase     =       StableDiffusionLatentUpscalePipeline
     _lowerCamelCase     =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         '''height''',
         '''width''',
         '''cross_attention_kwargs''',
         '''negative_prompt_embeds''',
         '''prompt_embeds''',
     }
     _lowerCamelCase     =       PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
     _lowerCamelCase     =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     _lowerCamelCase     =       frozenset(
         []     )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     _lowerCamelCase     =       frozenset([]     )

     _lowerCamelCase     =       True







     @property
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      1
      __magic_name__     =      4
      __magic_name__     =      (16, 16)

      __magic_name__     =      floats_tensor((batch_size, num_channels) + sizes ,						rng=random.Random(0       )       ).to(UpperCamelCase_       )
      return image







     def 	lowerCAmelCase__    (      self       ):
      torch.manual_seed(0       )
      __magic_name__     =      UNetaDConditionModel(
          act_fn='''gelu''' ,						attention_head_dim=8 ,						norm_num_groups=UpperCamelCase_ ,						block_out_channels=[32, 32, 64, 64] ,						time_cond_proj_dim=160 ,						conv_in_kernel=1 ,						conv_out_kernel=1 ,						cross_attention_dim=32 ,						down_block_types=(
              '''KDownBlock2D''',
              '''KCrossAttnDownBlock2D''',
              '''KCrossAttnDownBlock2D''',
              '''KCrossAttnDownBlock2D''',
          ) ,						in_channels=8 ,						mid_block_type=UpperCamelCase_ ,						only_cross_attention=UpperCamelCase_ ,						out_channels=5 ,						resnet_time_scale_shift='''scale_shift''' ,						time_embedding_type='''fourier''' ,						timestep_post_act='''gelu''' ,						up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''') ,						)
      __magic_name__     =      AutoencoderKL(
          block_out_channels=[32, 32, 64, 64] ,						in_channels=3 ,						out_channels=3 ,						down_block_types=[
              '''DownEncoderBlock2D''',
              '''DownEncoderBlock2D''',
              '''DownEncoderBlock2D''',
              '''DownEncoderBlock2D''',
          ] ,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,						latent_channels=4 ,						)
      __magic_name__     =      EulerDiscreteScheduler(prediction_type='''sample'''       )
      __magic_name__     =      CLIPTextConfig(
          bos_token_id=0 ,						eos_token_id=2 ,						hidden_size=32 ,						intermediate_size=37 ,						layer_norm_eps=1E-0_5 ,						num_attention_heads=4 ,						num_hidden_layers=5 ,						pad_token_id=1 ,						vocab_size=1000 ,						hidden_act='''quick_gelu''' ,						projection_dim=512 ,						)
      __magic_name__     =      CLIPTextModel(UpperCamelCase_       )
      __magic_name__     =      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

      __magic_name__     =      {
          '''unet''': model.eval(),
          '''vae''': vae.eval(),
          '''scheduler''': scheduler,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
      }

      return components







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_=0       ):
      if str(UpperCamelCase_       ).startswith('''mps'''       ):
       __magic_name__     =      torch.manual_seed(UpperCamelCase_       )
      else:
       __magic_name__     =      torch.Generator(device=UpperCamelCase_       ).manual_seed(UpperCamelCase_       )
      __magic_name__     =      {
          '''prompt''': '''A painting of a squirrel eating a burger''',
          '''image''': self.dummy_image.cpu(),
          '''generator''': generator,
          '''num_inference_steps''': 2,
          '''output_type''': '''numpy''',
      }
      return inputs







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      '''cpu'''

      __magic_name__     =      self.get_dummy_components()
      __magic_name__     =      self.pipeline_class(**UpperCamelCase_       )
      pipe.to(UpperCamelCase_       )
      pipe.set_progress_bar_config(disable=UpperCamelCase_       )

      __magic_name__     =      self.get_dummy_inputs(UpperCamelCase_       )
      __magic_name__     =      pipe(**UpperCamelCase_       ).images
      __magic_name__     =      image[0, -3:, -3:, -1]

      self.assertEqual(image.shape ,						(1, 256, 256, 3)       )
      __magic_name__     =      np.array(
          [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]       )
      __magic_name__     =      np.abs(image_slice.flatten() - expected_slice       ).max()
      self.assertLessEqual(UpperCamelCase_ ,						1E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_attention_slicing_forward_pass(expected_max_diff=7E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_cpu_offload_forward_pass(expected_max_diff=3E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_inference_batch_single_identical(expected_max_diff=7E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_save_load_local(expected_max_difference=3E-3       )







     def 	lowerCAmelCase__    (      self       ):
      super().test_save_load_optional_components(expected_max_difference=3E-3       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      [
          '''DDIMScheduler''',
          '''DDPMScheduler''',
          '''PNDMScheduler''',
          '''HeunDiscreteScheduler''',
          '''EulerAncestralDiscreteScheduler''',
          '''KDPM2DiscreteScheduler''',
          '''KDPM2AncestralDiscreteScheduler''',
          '''DPMSolverSDEScheduler''',
      ]
      __magic_name__     =      self.get_dummy_components()
      __magic_name__     =      self.pipeline_class(**UpperCamelCase_       )

      # make sure that PNDM does not need warm-up
      pipe.scheduler.register_to_config(skip_prk_steps=UpperCamelCase_       )

      pipe.to(UpperCamelCase_       )
      pipe.set_progress_bar_config(disable=UpperCamelCase_       )
      __magic_name__     =      self.get_dummy_inputs(UpperCamelCase_       )
      __magic_name__     =      2

      __magic_name__     =      []
      for scheduler_enum in KarrasDiffusionSchedulers:
       if scheduler_enum.name in skip_schedulers:
        # no sigma schedulers are not supported
        # no schedulers
        continue

       __magic_name__     =      getattr(UpperCamelCase_ ,						scheduler_enum.name       )
       __magic_name__     =      scheduler_cls.from_config(pipe.scheduler.config       )
       __magic_name__     =      pipe(**UpperCamelCase_       )[0]
       outputs.append(UpperCamelCase_       )

      assert check_same_shape(UpperCamelCase_       )


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







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







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      torch.manual_seed(33       )

      __magic_name__     =      StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' ,						torch_dtype=torch.floataa       )
      pipe.to('''cuda'''       )

      __magic_name__     =      StableDiffusionLatentUpscalePipeline.from_pretrained(
          '''stabilityai/sd-x2-latent-upscaler''' ,						torch_dtype=torch.floataa       )
      upscaler.to('''cuda'''       )

      __magic_name__     =      '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

      __magic_name__     =      pipe(UpperCamelCase_ ,						generator=UpperCamelCase_ ,						output_type='''latent'''       ).images

      __magic_name__     =      upscaler(
          prompt=UpperCamelCase_ ,						image=UpperCamelCase_ ,						num_inference_steps=20 ,						guidance_scale=0 ,						generator=UpperCamelCase_ ,						output_type='''np''' ,						).images[0]

      __magic_name__     =      load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''       )
      assert np.abs((expected_image - image).mean()       ) < 5E-2







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      torch.manual_seed(33       )

      __magic_name__     =      StableDiffusionLatentUpscalePipeline.from_pretrained(
          '''stabilityai/sd-x2-latent-upscaler''' ,						torch_dtype=torch.floataa       )
      upscaler.to('''cuda'''       )

      __magic_name__     =      '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

      __magic_name__     =      load_image(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''       )

      __magic_name__     =      upscaler(
          prompt=UpperCamelCase_ ,						image=UpperCamelCase_ ,						num_inference_steps=20 ,						guidance_scale=0 ,						generator=UpperCamelCase_ ,						output_type='''np''' ,						).images[0]

      __magic_name__     =      load_numpy(
          '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''       )
      assert np.abs((expected_image - image).max()       ) < 5E-2

