
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 						UpperCAmelCase	(		A_ ,A_ ,unittest.TestCase				):
   A__      :	str		  =  IFImgaImgPipeline
   A__      :	Optional[Any]		  =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
   A__      :	int		  =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   A__      :	str		  =  PipelineTesterMixin.required_optional_params - {"latents"}



   def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			str:


          '''simple docstring'''


          return self._get_dummy_components()



   def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]					,		snake_case__	:      Optional[Any]					,		snake_case__	:      int=0				)	->			Dict:


          '''simple docstring'''


          if str(snake_case__				).startswith("mps"				):
                 snake_case :     Tuple           =   torch.manual_seed(snake_case__				)
          else:
                 snake_case :     int           =   torch.Generator(device=snake_case__				).manual_seed(snake_case__				)

          snake_case :     Union[str, Any]           =   floats_tensor((1, 3, 32, 32)					,		rng=random.Random(snake_case__				)				).to(snake_case__				)

          snake_case :     int           =   {
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }

          return inputs



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


          '''simple docstring'''


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


          '''simple docstring'''


          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)



   @unittest.skipIf(torch_device != "cuda"					,		reason="float16 requires CUDA"				)
   def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			str:


          '''simple docstring'''


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



   @unittest.skipIf(torch_device != "cuda"					,		reason="float16 requires CUDA"				)
   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[Any]				)	->			Any:


          '''simple docstring'''


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



   def        _SCREAMING_SNAKE_CASE     (self	:      str				)	->			str:


          '''simple docstring'''


          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2				)



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


          '''simple docstring'''


          self._test_save_load_local()



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			Dict:


          '''simple docstring'''


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







import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand


def     UpperCamelCase      (					__lowerCamelCase  :				int					):
       return EnvironmentCommand()






class 						UpperCAmelCase	(		A_				):



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      ArgumentParser				)	->			Any:


          '''simple docstring'''


          snake_case :     Optional[Any]           =   parser.add_parser("env"					,		help="Print relevant system environment info."				)
          download_parser.set_defaults(func=snake_case__				)



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


          '''simple docstring'''


          snake_case :     List[str]           =   {
              "`datasets` version": version,
              "Platform": platform.platform(),
              "Python version": platform.python_version(),
              "Huggingface_hub version": huggingface_hub.__version__,
              "PyArrow version": pyarrow.__version__,
              "Pandas version": pandas.__version__,
          }

          print("\nCopy-and-paste the text below in your GitHub issue.\n"				)
          print(self.format_dict(snake_case__				)				)

          return info



   @staticmethod
   def        _SCREAMING_SNAKE_CASE     (snake_case__	:      Tuple				)	->			Any:


          '''simple docstring'''


          return "\n".join([f"""- {prop}: {val}""" for prop, val in d.items()]				) + "\n"





