
"""simple docstring"""





import importlib.metadata
import sys

from transformers.testing_utils import TestCasePlus
from transformers.utils.versions import require_version, require_version_core


_a									=				importlib.metadata.version('numpy')
_a									=				'.'.join([str(x) for x in sys.version_info[:3]])


class A_      (lowercase__ ):




  '''simple docstring'''







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     # lt + different version strings
     require_version_core("numpy<1000.4.5"    )
     require_version_core("numpy<1000.4"    )
     require_version_core("numpy<1000"    )

     # le
     require_version_core("numpy<=1000.4.5"    )
     require_version_core(F"""numpy<={numpy_ver}"""    )

     # eq
     require_version_core(F"""numpy=={numpy_ver}"""    )

     # ne
     require_version_core("numpy!=1000.4.5"    )

     # ge
     require_version_core("numpy>=1.0"    )
     require_version_core("numpy>=1.0.0"    )
     require_version_core(F"""numpy>={numpy_ver}"""    )

     # gt
     require_version_core("numpy>1.0.0"    )

     # mix
     require_version_core("numpy>1.0.0,<1000"    )

     # requirement w/o version
     require_version_core("numpy"    )

     # unmet requirements due to version conflict
     for req in ["numpy==1.0.0", "numpy>=1000.0.0", F"""numpy<{numpy_ver}"""]:
        try:
           require_version_core(lowercase_    )
        except ImportError as e:
           self.assertIn(F"""{req} is required"""							,  str(lowercase_    )    )
           self.assertIn("but found"							,  str(lowercase_    )    )

        # unmet requirements due to missing module
     for req in ["numpipypie>1", "numpipypie2"]:
        try:
           require_version_core(lowercase_    )
        except importlib.metadata.PackageNotFoundError as e:
           self.assertIn(F"""The '{req}' distribution was not found and is required by this application"""							,  str(lowercase_    )    )
           self.assertIn("Try: pip install transformers -U"							,  str(lowercase_    )    )

        # bogus requirements formats:
        # 1. whole thing
     for req in ["numpy??1.0.0", "numpy1.0.0"]:
        try:
           require_version_core(lowercase_    )
        except ValueError as e:
           self.assertIn("requirement needs to be in the pip package format"							,  str(lowercase_    )    )
        # 2. only operators
     for req in ["numpy=1.0.0", "numpy == 1.00", "numpy<>1.0.0", "numpy><1.00", "numpy>>1.0.0"]:
        try:
           require_version_core(lowercase_    )
        except ValueError as e:
           self.assertIn("need one of "							,  str(lowercase_    )    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     # matching requirement
     require_version("python>=3.6.0"    )

     # not matching requirements
     for req in ["python>9.9.9", "python<3.0.0"]:
        try:
           require_version_core(lowercase_    )
        except ImportError as e:
           self.assertIn(F"""{req} is required"""							,  str(lowercase_    )    )
           self.assertIn(F"""but found python=={python_ver}"""							,  str(lowercase_    )    )




"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			List[str]	=       IFSuperResolutionPipeline
    __lowercase				:			Optional[Any]	=       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        lowerCamelCase__						(     self  ):
      return self._get_superresolution_dummy_components()
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=0  ):
      if str(snake_case_  ).startswith("mps"  ):
        _snake_case  :    Tuple            = torch.manual_seed(snake_case_  )
      else:
        _snake_case  :    Tuple            = torch.Generator(device=snake_case_  ).manual_seed(snake_case_  )

      _snake_case  :    List[str]            = floats_tensor((1, 3, 32, 32)    ,     rng=random.Random(snake_case_  )  ).to(snake_case_  )

      _snake_case  :    List[str]            = {
          "prompt": "A painting of a squirrel eating a burger",
          "image": 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        lowerCamelCase__						(     self  ):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_optional_components()
    @unittest.skipIf(torch_device != "cuda"    ,     reason="float16 requires CUDA"  )
    def        lowerCamelCase__						(     self  ):
      # 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  )
    def        lowerCamelCase__						(     self  ):
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_local()


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

