


'''simple docstring'''







import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 		_snake_case    (				snake_case_  ,  snake_case_  ,  unittest.TestCase		):






						'''simple docstring'''







						__snake_case   =      IFImgaImgPipeline
						__snake_case   =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
						__snake_case   =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
						__snake_case   =      PipelineTesterMixin.required_optional_params - {"latents"}







						def lowerCAmelCase__ (  self:	Tuple     )							->	Optional[int]:
										return self._get_dummy_components()







						def lowerCAmelCase__ (  self:	Any      ,							__UpperCamelCase:	Union[str, Any]      ,							__UpperCamelCase:	Any=0     )							->	int:
										if str(__UpperCamelCase     ).startswith("mps"     ):
														__magic_name__				:					Optional[Any]						       =       torch.manual_seed(__UpperCamelCase     )
										else:
														__magic_name__				:					Union[str, Any]						       =       torch.Generator(device=__UpperCamelCase     ).manual_seed(__UpperCamelCase     )

										__magic_name__				:					Dict						       =       floats_tensor((1, 3, 32, 32)      ,							rng=random.Random(__UpperCamelCase     )     ).to(__UpperCamelCase     )

										__magic_name__				:					Union[str, Any]						       =       {
										    "prompt": "A painting of a squirrel eating a burger",
										    "image": image,
										    "generator": generator,
										    "num_inference_steps": 2,
										    "output_type": "numpy",
										}

										return inputs







						def lowerCAmelCase__ (  self:	Dict     )							->	Optional[Any]:
										self._test_save_load_optional_components()







						@unittest.skipIf(
						    torch_device != "cuda" or not is_xformers_available()      ,							reason="XFormers attention is only available with CUDA and `xformers` installed"      ,							)
						def lowerCAmelCase__ (  self:	Tuple     )							->	Optional[Any]:
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )







						@unittest.skipIf(torch_device != "cuda"      ,							reason="float16 requires CUDA"     )
						def lowerCAmelCase__ (  self:	int     )							->	Optional[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     )







						@unittest.skipIf(torch_device != "cuda"      ,							reason="float16 requires CUDA"     )
						def lowerCAmelCase__ (  self:	Dict     )							->	int:
										super().test_floataa_inference(expected_max_diff=1E-1     )







						def lowerCAmelCase__ (  self:	Any     )							->	Dict:
										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )







						def lowerCAmelCase__ (  self:	Tuple     )							->	int:
										self._test_save_load_local()







						def lowerCAmelCase__ (  self:	Optional[int]     )							->	Any:
										self._test_inference_batch_single_identical(
										    expected_max_diff=1E-2      ,							)



'''simple docstring'''







from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE       :   Any        = {
    "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config", "DebertaV2OnnxConfig"],
    "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   List[Any]        = ["DebertaV2TokenizerFast"]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   int        = [
		    "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFDebertaV2ForMaskedLM",
		    "TFDebertaV2ForQuestionAnswering",
		    "TFDebertaV2ForSequenceClassification",
		    "TFDebertaV2ForTokenClassification",
		    "TFDebertaV2Model",
		    "TFDebertaV2PreTrainedModel",
		]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   Union[str, Any]        = [
		    "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DebertaV2ForMaskedLM",
		    "DebertaV2ForMultipleChoice",
		    "DebertaV2ForQuestionAnswering",
		    "DebertaV2ForSequenceClassification",
		    "DebertaV2ForTokenClassification",
		    "DebertaV2Model",
		    "DebertaV2PreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_deberta_va import (
		    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DebertaVaConfig,
		    DebertaVaOnnxConfig,
		)
		from .tokenization_deberta_va import DebertaVaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deberta_va import (
				    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDebertaVaForMaskedLM,
				    TFDebertaVaForQuestionAnswering,
				    TFDebertaVaForSequenceClassification,
				    TFDebertaVaForTokenClassification,
				    TFDebertaVaModel,
				    TFDebertaVaPreTrainedModel,
				)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deberta_va import (
				    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DebertaVaForMaskedLM,
				    DebertaVaForMultipleChoice,
				    DebertaVaForQuestionAnswering,
				    DebertaVaForSequenceClassification,
				    DebertaVaForTokenClassification,
				    DebertaVaModel,
				    DebertaVaPreTrainedModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE       :   Dict        = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)