




"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


A				=							logging.get_logger(__name__)

A				=							{
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}



class   a__					(						__magic_name__ ):
			lowercase_		=						"mra"


			def __init__(   self   :     Any   ,	UpperCamelCase_   :     List[Any]=50265   ,	UpperCamelCase_   :     List[Any]=768   ,	UpperCamelCase_   :     Dict=12   ,	UpperCamelCase_   :     int=12   ,	UpperCamelCase_   :     str=3072   ,	UpperCamelCase_   :     Optional[int]="gelu"   ,	UpperCamelCase_   :     Union[str, Any]=0.1   ,	UpperCamelCase_   :     List[Any]=0.1   ,	UpperCamelCase_   :     Dict=512   ,	UpperCamelCase_   :     Optional[int]=1   ,	UpperCamelCase_   :     Optional[Any]=0.02   ,	UpperCamelCase_   :     List[str]=1e-5   ,	UpperCamelCase_   :     str="absolute"   ,	UpperCamelCase_   :     Tuple=4   ,	UpperCamelCase_   :     Any="full"   ,	UpperCamelCase_   :     List[Any]=0   ,	UpperCamelCase_   :     List[Any]=0   ,	UpperCamelCase_   :     Tuple=1   ,	UpperCamelCase_   :     Dict=0   ,	UpperCamelCase_   :     int=2   ,	**UpperCamelCase_   :     Optional[Any]   ,	):


						"""simple docstring"""




						super().__init__(pad_token_id=UpperCamelCase_   ,	bos_token_id=UpperCamelCase_   ,	eos_token_id=UpperCamelCase_   ,	**UpperCamelCase_)

						__UpperCAmelCase						:			str												=       vocab_size
						__UpperCAmelCase						:			int												=       max_position_embeddings
						__UpperCAmelCase						:			Dict												=       hidden_size
						__UpperCAmelCase						:			List[str]												=       num_hidden_layers
						__UpperCAmelCase						:			Any												=       num_attention_heads
						__UpperCAmelCase						:			int												=       intermediate_size
						__UpperCAmelCase						:			Tuple												=       hidden_act
						__UpperCAmelCase						:			Optional[Any]												=       hidden_dropout_prob
						__UpperCAmelCase						:			int												=       attention_probs_dropout_prob
						__UpperCAmelCase						:			Union[str, Any]												=       initializer_range
						__UpperCAmelCase						:			List[Any]												=       type_vocab_size
						__UpperCAmelCase						:			Dict												=       layer_norm_eps
						__UpperCAmelCase						:			Optional[Any]												=       position_embedding_type
						__UpperCAmelCase						:			Any												=       block_per_row
						__UpperCAmelCase						:			Optional[int]												=       approx_mode
						__UpperCAmelCase						:			int												=       initial_prior_first_n_blocks
						__UpperCAmelCase						:			Tuple												=       initial_prior_diagonal_n_blocks









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   ,				)
