





import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand

def   __snake_case    (       _lowerCAmelCase			:			Optional[Any]						)			->    List[str]:
   return EnvironmentCommand()





class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     @staticmethod
     def     SCREAMING_SNAKE_CASE		(				snake_case							:ArgumentParser	):





        '''simple docstring'''




        A_       :					Union[str, Any]        =      parser.add_parser("env"	)
        download_parser.set_defaults(func=snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        A_       :					Dict        =      huggingface_hub.__version__

        A_       :					Tuple        =      "not installed"
        A_       :					int        =      "NA"
        if is_torch_available():
           import torch

           A_       :					List[str]        =      torch.__version__
           A_       :					Optional[int]        =      torch.cuda.is_available()

        A_       :					int        =      "not installed"
        if is_transformers_available():
           import transformers

           A_       :					Optional[Any]        =      transformers.__version__

        A_       :					Tuple        =      "not installed"
        if is_accelerate_available():
           import accelerate

           A_       :					Optional[int]        =      accelerate.__version__

        A_       :					Dict        =      "not installed"
        if is_xformers_available():
           import xformers

           A_       :					Tuple        =      xformers.__version__

        A_       :					Optional[int]        =      {
            "`diffusers` version": version,
            "Platform": platform.platform(),
            "Python version": platform.python_version(),
            "PyTorch version (GPU?)": f"{pt_version} ({pt_cuda_available})",
            "Huggingface_hub version": hub_version,
            "Transformers version": transformers_version,
            "Accelerate version": accelerate_version,
            "xFormers version": xformers_version,
            "Using GPU in script?": "<fill in>",
            "Using distributed or parallel set-up in script?": "<fill in>",
        }

        print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"	)
        print(self.format_dict(snake_case	)	)

        return info





     @staticmethod
     def     SCREAMING_SNAKE_CASE		(				snake_case							:Dict	):





        '''simple docstring'''




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







import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        __magic_name__	(				lowerCamelCase__     ,					lowerCamelCase__     ,					unittest.TestCase				):





     """simple docstring"""



     __UpperCamelCase            =  IFInpaintingPipeline
     __UpperCamelCase            =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
     __UpperCamelCase            =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     __UpperCamelCase            =  PipelineTesterMixin.required_optional_params - {'''latents'''}



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        return self._get_dummy_components()



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]      ,   snake_case							:str      ,   snake_case							:List[Any]=0	):





        '''simple docstring'''




        if str(snake_case	).startswith("mps"	):
           A_       :					Optional[Any]        =      torch.manual_seed(snake_case	)
        else:
           A_       :					Any        =      torch.Generator(device=snake_case	).manual_seed(snake_case	)

        A_       :					Optional[Any]        =      floats_tensor((1, 3, 32, 32)      ,   rng=random.Random(snake_case	)	).to(snake_case	)
        A_       :					Any        =      floats_tensor((1, 3, 32, 32)      ,   rng=random.Random(snake_case	)	).to(snake_case	)

        A_       :					List[Any]        =      {
            "prompt": "A painting of a squirrel eating a burger",
            "image": image,
            "mask_image": mask_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     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        self._test_save_load_optional_components()



     @unittest.skipIf(torch_device != "cuda"      ,   reason="float16 requires CUDA"	)
     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        self._test_save_load_local()





     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




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