





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 							__snake_case							(    lowerCAmelCase_	)				->						Union[str, Any]:
    return EnvironmentCommand()


def 							__snake_case							(    lowerCAmelCase_	)				->						Tuple:
    return EnvironmentCommand(args.accelerate_config_file	)



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    @staticmethod
    def  lowercase_					(       A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			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   ,				A_   ,				*A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			accelerate_config_file



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			'''not installed'''
        if is_safetensors_available():
            import safetensors

            SCREAMING_SNAKE_CASE__											=			safetensors.__version__
        elif importlib.util.find_spec('''safetensors'''     ) is not None:
            import safetensors

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

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

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

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

        SCREAMING_SNAKE_CASE__											=			'''not installed'''
        SCREAMING_SNAKE_CASE__											=			'''NA'''
        if is_torch_available():
            import torch

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

        SCREAMING_SNAKE_CASE__											=			'''not installed'''
        SCREAMING_SNAKE_CASE__											=			'''NA'''
        if is_tf_available():
            import tensorflow as tf

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

        SCREAMING_SNAKE_CASE__											=			'''not installed'''
        SCREAMING_SNAKE_CASE__											=			'''not installed'''
        SCREAMING_SNAKE_CASE__											=			'''not installed'''
        SCREAMING_SNAKE_CASE__											=			'''NA'''
        if is_flax_available():
            import flax
            import jax
            import jaxlib

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

        SCREAMING_SNAKE_CASE__											=			{
            '''`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  lowercase_					(       A_     ):





        '''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 					__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   ,				)
