





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    ( lowerCAmelCase__	, lowerCAmelCase__	, unittest.TestCase  ):







	'''simple docstring'''

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


	def   _UpperCAmelCase       (self	)							->							Union[str, Any]:







				'''simple docstring'''





				return self._get_dummy_components()


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=0	)							->							Any:







				'''simple docstring'''





				if str(_lowerCamelCase	).startswith('''mps'''	):
							__lowercase			  =							torch.manual_seed(_lowerCamelCase	)
				else:
							__lowercase			  =							torch.Generator(device=_lowerCamelCase	).manual_seed(_lowerCamelCase	)

				__lowercase			  =							floats_tensor((1, 3, 32, 32)       ,rng=random.Random(_lowerCamelCase	)	).to(_lowerCamelCase	)
				__lowercase			  =							floats_tensor((1, 3, 32, 32)       ,rng=random.Random(_lowerCamelCase	)	).to(_lowerCamelCase	)

				__lowercase			  =							{
				    '''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	)							->							Optional[int]:







				'''simple docstring'''





				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3	)


	def   _UpperCAmelCase       (self	)							->							Optional[Any]:







				'''simple docstring'''





				self._test_save_load_optional_components()


	@unittest.skipIf(torch_device != '''cuda'''       ,reason='''float16 requires CUDA'''	)
	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				super().test_save_load_floataa(expected_max_diff=1E-1	)


	def   _UpperCAmelCase       (self	)							->							Optional[int]:







				'''simple docstring'''





				self._test_attention_slicing_forward_pass(expected_max_diff=1E-2	)


	def   _UpperCAmelCase       (self	)							->							Optional[int]:







				'''simple docstring'''





				self._test_save_load_local()


	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				self._test_inference_batch_single_identical(
				    expected_max_diff=1E-2       ,)












'''simple docstring'''



from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE   =      {
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''BlenderbotForCausalLM''',
	    '''BlenderbotForConditionalGeneration''',
	    '''BlenderbotModel''',
	    '''BlenderbotPreTrainedModel''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''TFBlenderbotForConditionalGeneration''',
	    '''TFBlenderbotModel''',
	    '''TFBlenderbotPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''FlaxBlenderbotForConditionalGeneration''',
	    '''FlaxBlenderbotModel''',
	    '''FlaxBlenderbotPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

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





