


"""simple docstring"""






import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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 	A_       (     snake_case__      ,  snake_case__      ,  unittest.TestCase   ):





  """simple docstring"""

  lowercase		:	Optional[Any]   =						IFImgaImgPipeline
  lowercase		:	Dict   =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
  lowercase		:	List[Any]   =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  lowercase		:	Optional[Any]   =						PipelineTesterMixin.required_optional_params - {'latents'}
  def 							lowercase_     (						self						)				->					Any:
     return self._get_dummy_components()
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase=0						)				->					Tuple:
     if str(lowerCAmelCase_						).startswith('mps'						):
        a   :	List[str]						=    torch.manual_seed(lowerCAmelCase_						)
     else:
        a   :	Optional[Any]						=    torch.Generator(device=lowerCAmelCase_						).manual_seed(lowerCAmelCase_						)

     a   :	List[Any]						=    floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(lowerCAmelCase_						)						).to(lowerCAmelCase_						)

     a   :	List[str]						=    {
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''output_type''': '''numpy''',
     }

     return inputs
  def 							lowercase_     (						self						)				->					Optional[Any]:
     self._test_save_load_optional_components()
  @unittest.skipIf(
      torch_device != 'cuda' or not is_xformers_available()  ,					reason='XFormers attention is only available with CUDA and `xformers` installed'  ,					)
  def 							lowercase_     (						self						)				->					Any:
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)
  @unittest.skipIf(torch_device != 'cuda'  ,					reason='float16 requires CUDA'						)
  def 							lowercase_     (						self						)				->					Tuple:
     # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
     super().test_save_load_floataa(expected_max_diff=1E-1						)
  @unittest.skipIf(torch_device != 'cuda'  ,					reason='float16 requires CUDA'						)
  def 							lowercase_     (						self						)				->					Union[str, Any]:
     super().test_floataa_inference(expected_max_diff=1E-1						)
  def 							lowercase_     (						self						)				->					Optional[int]:
     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)
  def 							lowercase_     (						self						)				->					List[str]:
     self._test_save_load_local()






  def 							lowercase_     (						self						)				->					int:
     self._test_inference_batch_single_identical(
         expected_max_diff=1E-2  ,					)






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata







def 						UpperCAmelCase							(		lowerCAmelCase_							)						->						str:
     a	:     Optional[Any]						      =    min(re.search('''^ +'''    ,   lowerCAmelCase_							).end() if t.startswith(''' '''							) else 0 for t in string.splitlines()							)
     return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(lowerCAmelCase_							)]							)


UpperCAmelCase				 =   """\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
"""

UpperCAmelCase				 =   """\
---
---
# Begin of markdown

Some cool dataset card
"""


UpperCAmelCase				 =   """\
# Begin of markdown

Some cool dataset card
"""






class 				_SCREAMING_SNAKE_CASE( unittest.TestCase						):
   def _lowercase(     self     )						->List[str]:
        with tempfile.TemporaryDirectory() as tmp_dir:
             a	:     List[str]						      =    Path(lowerCAmelCase_     ) / '''README.md'''
             with open(lowerCAmelCase_				,  '''w+'''     ) as readme_file:
                  readme_file.write(lowerCAmelCase_     )
             a	:     Dict						      =    DatasetMetadata.from_readme(lowerCAmelCase_     )
             self.assertDictEqual(lowerCAmelCase_				,  {'''language''': ['''zh''', '''en'''], '''task_ids''': ['''sentiment-classification''']}     )

             with open(lowerCAmelCase_				,  '''w+'''     ) as readme_file:
                  readme_file.write(lowerCAmelCase_     )
             a	:     List[str]						      =    DatasetMetadata.from_readme(lowerCAmelCase_     )
             self.assertDictEqual(lowerCAmelCase_				,  {}     )

             with open(lowerCAmelCase_				,  '''w+'''     ) as readme_file:
                  readme_file.write(lowerCAmelCase_     )
             a	:     List[Any]						      =    DatasetMetadata.from_readme(lowerCAmelCase_     )
             self.assertEqual(lowerCAmelCase_				,  {}     )


   def _lowercase(     self     )						->Optional[Any]:
        a	:     Tuple						      =    _dedent(
            '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            '''     )
        assert DatasetMetadata.from_yaml_string(lowerCAmelCase_     )

        a	:     Dict						      =    _dedent(
            '''\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            '''     )
        with self.assertRaises(lowerCAmelCase_     ):
             DatasetMetadata.from_yaml_string(lowerCAmelCase_     )

        a	:     Optional[int]						      =    _dedent(
            '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            '''     )
        assert DatasetMetadata.from_yaml_string(lowerCAmelCase_     )