




'''simple docstring'''




import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class   __UpperCamelCase       ( _A			,					_A			,					unittest.TestCase				):
       lowercase	:    str      						=IFImgaImgSuperResolutionPipeline
       lowercase	:    Tuple      						=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
       lowercase	:    Optional[int]      						=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}				)
       lowercase	:    List[str]      						=PipelineTesterMixin.required_optional_params - {'latents'}






       def      lowercase__     (							self  ):



              """simple docstring"""







              return self._get_superresolution_dummy_components()






       def      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase=0  ):



              """simple docstring"""







              if str(UpperCamelCase__  ).startswith('''mps'''  ):
                     lowerCamelCase_							=torch.manual_seed(UpperCamelCase__  )
              else:
                     lowerCamelCase_							=torch.Generator(device=UpperCamelCase__  ).manual_seed(UpperCamelCase__  )

              lowerCamelCase_							=floats_tensor((1, 3, 32, 32),		rng=random.Random(UpperCamelCase__  )  ).to(UpperCamelCase__  )
              lowerCamelCase_							=floats_tensor((1, 3, 16, 16),		rng=random.Random(UpperCamelCase__  )  ).to(UpperCamelCase__  )

              lowerCamelCase_							={
                  '''prompt''': '''A painting of a squirrel eating a burger''',
                  '''image''': image,
                  '''original_image''': original_image,
                  '''generator''': generator,
                  '''num_inference_steps''': 2,
                  '''output_type''': '''numpy''',
              }

              return inputs






       @unittest.skipIf(
           torch_device != '''cuda''' or not is_xformers_available(),		reason='''XFormers attention is only available with CUDA and `xformers` installed''',		)
       def      lowercase__     (							self  ):



              """simple docstring"""







              self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              self._test_save_load_optional_components()






       @unittest.skipIf(torch_device != '''cuda''',		reason='''float16 requires CUDA'''  )
       def      lowercase__     (							self  ):



              """simple docstring"""







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






       def      lowercase__     (							self  ):



              """simple docstring"""







              self._test_attention_slicing_forward_pass(expected_max_diff=1e-2  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              self._test_save_load_local()






       def      lowercase__     (							self  ):



              """simple docstring"""







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






import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


__lowerCAmelCase			:     Any           =   [
    {'dataset': 'wikipedia', 'config_name': '20220301.de'},
    {'dataset': 'wikipedia', 'config_name': '20220301.en'},
    {'dataset': 'wikipedia', 'config_name': '20220301.fr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.frr'},
    {'dataset': 'wikipedia', 'config_name': '20220301.it'},
    {'dataset': 'wikipedia', 'config_name': '20220301.simple'},
    {'dataset': 'snli', 'config_name': 'plain_text'},
    {'dataset': 'eli5', 'config_name': 'LFQA_reddit'},
    {'dataset': 'wiki40b', 'config_name': 'en'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.compressed'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.nq.no_index'},
    {'dataset': 'wiki_dpr', 'config_name': 'psgs_w100.multiset.no_index'},
    {'dataset': 'natural_questions', 'config_name': 'default'},
]

def a__    (       A_=True					):






  '''simple docstring'''





  if with_config:
    return [
        {
            "testcase_name": d["dataset"] + "/" + d["config_name"],
            "dataset": d["dataset"],
            "config_name": d["config_name"],
        }
        for d in DATASETS_ON_HF_GCP
    ]
  else:
    return [
        {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
    ]




@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=_A     )     )
class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		None
  a__           =		None
  def 							_lowercase							( self			: List[Any]			,							UpperCamelCase__			: int			,							UpperCamelCase__			: Union[str, Any]    )			->	Tuple:







    """simple docstring"""






    with TemporaryDirectory() as tmp_dir:
      __magic_name__				     =       dataset_module_factory(UpperCamelCase__			,							cache_dir=UpperCamelCase__    )

      __magic_name__				     =       import_main_class(dataset_module.module_path			,							dataset=UpperCamelCase__    )

      __magic_name__				     =       builder_cls(
          cache_dir=UpperCamelCase__			,							config_name=UpperCamelCase__			,							hash=dataset_module.hash			,							)

      __magic_name__				     =       """/""".join(
          [
              HF_GCP_BASE_URL,
              builder_instance._relative_data_dir(with_hash=UpperCamelCase__    ).replace(os.sep			,							"""/"""    ),
              config.DATASET_INFO_FILENAME,
          ]    )
      __magic_name__				     =       cached_path(UpperCamelCase__			,							cache_dir=UpperCamelCase__    )
      self.assertTrue(os.path.exists(UpperCamelCase__    )    )







@pytest.mark.integration
def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       tmp_path_factory.mktemp("""test_hf_gcp"""					) / """test_wikipedia_simple"""
  __magic_name__				     =       dataset_module_factory("""wikipedia""",     cache_dir=A_					)
  __magic_name__				     =       import_main_class(dataset_module.module_path					)
  __magic_name__				     =       builder_cls(
      cache_dir=A_,     config_name="""20220301.frr""",     hash=dataset_module.hash,     )
  # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
  __magic_name__				     =       None
  builder_instance.download_and_prepare()
  __magic_name__				     =       builder_instance.as_dataset()
  assert ds





@pytest.mark.integration
def a__    (       A_					):






  '''simple docstring'''





  __magic_name__				     =       dataset_module_factory("""wikipedia""",     cache_dir=A_					)
  __magic_name__				     =       import_main_class(dataset_module.module_path,     dataset=A_					)
  __magic_name__				     =       builder_cls(
      cache_dir=A_,     config_name="""20220301.frr""",     hash=dataset_module.hash,     )
  __magic_name__				     =       builder_instance.as_streaming_dataset()
  assert ds
  assert isinstance(A_,     A_					)
  assert "train" in ds
  assert isinstance(ds["""train"""],     A_					)
  assert next(iter(ds["""train"""]					)					)

