
import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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							):



	"""simple docstring"""




	a			:str         =	IFInpaintingSuperResolutionPipeline
	a			:Optional[int]         =	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
	a			:int         =	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}							)
	a			:Optional[Any]         =	PipelineTesterMixin.required_optional_params - {'latents'}
	def 						_lowercase  (       self  :			List[Any]       )       ->     List[str]:
					return self._get_superresolution_dummy_components()
	def 						_lowercase  (       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=0       )       ->     int:
					if str(SCREAMING_SNAKE_CASE_       ).startswith('''mps'''       ):
									lowercase_       =					torch.manual_seed(SCREAMING_SNAKE_CASE_       )
					else:
									lowercase_       =					torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					floats_tensor((1, 3, 1_6, 1_6)       ,							rng=random.Random(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					floats_tensor((1, 3, 3_2, 3_2)       ,							rng=random.Random(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					floats_tensor((1, 3, 3_2, 3_2)       ,							rng=random.Random(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					{
					    '''prompt''': '''A painting of a squirrel eating a burger''',
					    '''image''': image,
					    '''original_image''': original_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  :			List[Any]       )       ->     Union[str, Any]:
					self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )
	def 						_lowercase  (       self  :			Optional[Any]       )       ->     Optional[Any]:
					self._test_save_load_optional_components()
	@unittest.skipIf(torch_device != '''cuda'''       ,							reason='''float16 requires CUDA'''       )
	def 						_lowercase  (       self  :			str       )       ->     Union[str, Any]:
					# 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 						_lowercase  (       self  :			Tuple       )       ->     List[str]:
					self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )
	def 						_lowercase  (       self  :			str       )       ->     Tuple:
					self._test_save_load_local()




	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     List[str]:
					self._test_inference_batch_single_identical(
					    expected_max_diff=1e-2       ,							)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a  			=					{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'RemBertForCausalLM',
						    'RemBertForMaskedLM',
						    'RemBertForMultipleChoice',
						    'RemBertForQuestionAnswering',
						    'RemBertForSequenceClassification',
						    'RemBertForTokenClassification',
						    'RemBertLayer',
						    'RemBertModel',
						    'RemBertPreTrainedModel',
						    'load_tf_weights_in_rembert',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFRemBertForCausalLM',
						    'TFRemBertForMaskedLM',
						    'TFRemBertForMultipleChoice',
						    'TFRemBertForQuestionAnswering',
						    'TFRemBertForSequenceClassification',
						    'TFRemBertForTokenClassification',
						    'TFRemBertLayer',
						    'TFRemBertModel',
						    'TFRemBertPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert import RemBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert_fast import RemBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rembert import (
												    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RemBertForCausalLM,
												    RemBertForMaskedLM,
												    RemBertForMultipleChoice,
												    RemBertForQuestionAnswering,
												    RemBertForSequenceClassification,
												    RemBertForTokenClassification,
												    RemBertLayer,
												    RemBertModel,
												    RemBertPreTrainedModel,
												    load_tf_weights_in_rembert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rembert import (
												    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRemBertForCausalLM,
												    TFRemBertForMaskedLM,
												    TFRemBertForMultipleChoice,
												    TFRemBertForQuestionAnswering,
												    TFRemBertForSequenceClassification,
												    TFRemBertForTokenClassification,
												    TFRemBertLayer,
												    TFRemBertModel,
												    TFRemBertPreTrainedModel,
												)


else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




