"""simple docstring"""


import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   Union[str, Any]:




						'''simple docstring'''



						return EnvironmentCommand()





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   Tuple:




						'''simple docstring'''



						return EnvironmentCommand(args.accelerate_config_file							)






class   __UpperCAmelCase		(      __SCREAMING_SNAKE_CASE      ):

				@staticmethod
				def 						_a      ( _lowerCamelCase ):
										lowerCamelCase__   =parser.add_parser("env" )
										download_parser.set_defaults(func=A_ )
										download_parser.add_argument(
										    "--accelerate-config_file"      ,					default=A_      ,					help="The accelerate config file to use for the default values in the launching script."      ,					)
										download_parser.set_defaults(func=A_ )

				def __init__( self      ,					_lowerCamelCase      ,					*_lowerCamelCase ):
										lowerCamelCase__   =accelerate_config_file

				def 						_a      ( self ):
										lowerCamelCase__   ="not installed"
										if is_safetensors_available():
																import safetensors

																lowerCamelCase__   =safetensors.__version__
										elif importlib.util.find_spec("safetensors" ) is not None:
																import safetensors

																lowerCamelCase__   =F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

										lowerCamelCase__   ="not installed"
										lowerCamelCase__   =lowerCamelCase__   ="not found"
										if is_accelerate_available():
																import accelerate
																from accelerate.commands.config import default_config_file, load_config_from_file

																lowerCamelCase__   =accelerate.__version__
																# Get the default from the config file.
																if self._accelerate_config_file is not None or os.path.isfile(A_ ):
																						lowerCamelCase__   =load_config_from_file(self._accelerate_config_file ).to_dict()

																lowerCamelCase__   =(
																    "\n".join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()] )
																    if isinstance(A_      ,					A_ )
																    else F'''\t{accelerate_config}'''
																)

										lowerCamelCase__   ="not installed"
										lowerCamelCase__   ="NA"
										if is_torch_available():
																import torch

																lowerCamelCase__   =torch.__version__
																lowerCamelCase__   =torch.cuda.is_available()

										lowerCamelCase__   ="not installed"
										lowerCamelCase__   ="NA"
										if is_tf_available():
																import tensorflow as tf

																lowerCamelCase__   =tf.__version__
																try:
																						# deprecated in v2.1
																						lowerCamelCase__   =tf.test.is_gpu_available()
																except AttributeError:
																						# returns list of devices, convert to bool
																						lowerCamelCase__   =bool(tf.config.list_physical_devices("GPU" ) )

										lowerCamelCase__   ="not installed"
										lowerCamelCase__   ="not installed"
										lowerCamelCase__   ="not installed"
										lowerCamelCase__   ="NA"
										if is_flax_available():
																import flax
																import jax
																import jaxlib

																lowerCamelCase__   =flax.__version__
																lowerCamelCase__   =jax.__version__
																lowerCamelCase__   =jaxlib.__version__
																lowerCamelCase__   =jax.lib.xla_bridge.get_backend().platform

										lowerCamelCase__   ={
										    "`transformers` version": version,
										    "Platform": platform.platform(),
										    "Python version": platform.python_version(),
										    "Huggingface_hub version": huggingface_hub.__version__,
										    "Safetensors version": F'''{safetensors_version}''',
										    "Accelerate version": F'''{accelerate_version}''',
										    "Accelerate config": F'''{accelerate_config_str}''',
										    "PyTorch version (GPU?)": F'''{pt_version} ({pt_cuda_available})''',
										    "Tensorflow version (GPU?)": F'''{tf_version} ({tf_cuda_available})''',
										    "Flax version (CPU?/GPU?/TPU?)": F'''{flax_version} ({jax_backend})''',
										    "Jax version": F'''{jax_version}''',
										    "JaxLib version": F'''{jaxlib_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(A_ ) )

										return info






				@staticmethod
				def 						_a      ( _lowerCamelCase ):
										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 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Any				=	IFInpaintingPipeline
    lowerCamelCase__    :		str				=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
    lowerCamelCase__    :		Optional[Any]				=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    lowerCamelCase__    :		Tuple				=	PipelineTesterMixin.required_optional_params - {"""latents"""}



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self._get_dummy_components()



    def  lowercase_					(       self   ,				A_   ,				A_=0     ):





        '''simple docstring'''

        if str(A_     ).startswith('''mps'''     ):
            SCREAMING_SNAKE_CASE__											=			torch.manual_seed(A_     )
        else:
            SCREAMING_SNAKE_CASE__											=			torch.Generator(device=A_     ).manual_seed(A_     )

        SCREAMING_SNAKE_CASE__											=			floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(A_     )     ).to(A_     )
        SCREAMING_SNAKE_CASE__											=			floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(A_     )     ).to(A_     )

        SCREAMING_SNAKE_CASE__											=			{
            '''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  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   ,				)
