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        lowercase_   (					_UpperCAmelCase						,			_UpperCAmelCase						,			unittest.TestCase		):
							_lowerCamelCase						=		IFInpaintingPipeline
							_lowerCamelCase						=		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
							_lowerCamelCase						=		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							_lowerCamelCase						=		PipelineTesterMixin.required_optional_params - {"latents"}



							def 	UpperCamelCase	(						self    ):
													return self._get_dummy_components()



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_=0    ):
													if str(_UpperCAmelCase    ).startswith("mps"    ):
																			_snake_case						:					Dict    =   torch.manual_seed(_UpperCAmelCase    )
													else:
																			_snake_case						:					Optional[int]    =   torch.Generator(device=_UpperCAmelCase    ).manual_seed(_UpperCAmelCase    )

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

													_snake_case						:					str    =   {
													    '''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 	UpperCamelCase	(						self    ):
													self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )



							def 	UpperCamelCase	(						self    ):
													self._test_save_load_optional_components()



							@unittest.skipIf(torch_device != "cuda"				,    reason="float16 requires CUDA"    )
							def 	UpperCamelCase	(						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 	UpperCamelCase	(						self    ):
													self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )



							def 	UpperCamelCase	(						self    ):
													self._test_save_load_local()



							def 	UpperCamelCase	(						self    ):
													self._test_inference_batch_single_identical(
													    expected_max_diff=1e-2				,    )
from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
				import tensorflow as tf


__SCREAMING_SNAKE_CASE						:   str         =			logging.get_logger(__name__)



@dataclass
class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		[
							    'no_inference',
							    'no_cuda',
							    'no_tpu',
							    'no_speed',
							    'no_memory',
							    'no_env_print',
							    'no_multi_process',
							]



							def __init__(						self				,    **lowercase_    ):
													for deprecated_arg in self.deprecated_args:
																			if deprecated_arg in kwargs:
																									_snake_case						:					List[str]    =   deprecated_arg[3:]
																									_snake_case						:					Optional[int]    =   not kwargs.pop(lowercase_    )
																									logger.warning(
																									    f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																									    f""" {positive_arg}={kwargs[positive_arg]}"""    )
													_snake_case						:					Tuple    =   kwargs.pop("tpu_name"				,    self.tpu_name    )
													_snake_case						:					Any    =   kwargs.pop("device_idx"				,    self.device_idx    )
													_snake_case						:					List[str]    =   kwargs.pop("eager_mode"				,    self.eager_mode    )
													_snake_case						:					List[str]    =   kwargs.pop("use_xla"				,    self.use_xla    )
													super().__init__(**lowercase_    )

							_lowerCamelCase						=		field(
							    default=__snake_case						,			metadata={'help': 'Name of TPU'}						,			)
							_lowerCamelCase						=		field(
							    default=0						,			metadata={'help': 'CPU / GPU device index. Defaults to 0.'}						,			)
							_lowerCamelCase						=		field(default=__snake_case						,			metadata={'help': 'Benchmark models in eager model.'}		)
							_lowerCamelCase						=		field(
							    default=__snake_case						,			metadata={
							        'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
							    }						,			)



							@cached_property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													_snake_case						:					str    =   None
													if self.tpu:
																			try:
																									if self.tpu_name:
																															_snake_case						:					Optional[Any]    =   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name    )
																									else:
																															_snake_case						:					List[str]    =   tf.distribute.cluster_resolver.TPUClusterResolver()
																			except ValueError:
																									_snake_case						:					Union[str, Any]    =   None
													return tpu



							@cached_property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													if self.is_tpu:
																			tf.config.experimental_connect_to_cluster(self._setup_tpu    )
																			tf.tpu.experimental.initialize_tpu_system(self._setup_tpu    )

																			_snake_case						:					List[str]    =   tf.distribute.TPUStrategy(self._setup_tpu    )
													else:
																			# currently no multi gpu is allowed
																			if self.is_gpu:
																									# TODO: Currently only single GPU is supported
																									tf.config.set_visible_devices(self.gpu_list[self.device_idx]				,    "GPU"    )
																									_snake_case						:					Any    =   tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""    )
																			else:
																									tf.config.set_visible_devices([]				,    "GPU"    )  # disable GPU
																									_snake_case						:					Any    =   tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""    )

													return strategy



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return self._setup_tpu is not None



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return self._setup_strategy



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return tf.config.list_physical_devices("GPU"    )



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													if self.cuda:
																			return len(self.gpu_list    )
													return 0



							@property
							def 	UpperCamelCase	(						self    ):
													return self.n_gpu > 0