



"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase							:       Optional[Any]          =       logging.get_logger(__name__)

__lowerCAmelCase							:       int          =       {
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}



class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							'''data2vec-text'''





		def __init__(       self				,     _lowercase=3_0_5_2_2				,     _lowercase=7_6_8				,     _lowercase=1_2				,     _lowercase=1_2				,     _lowercase=3_0_7_2				,     _lowercase="gelu"				,     _lowercase=0.1				,     _lowercase=0.1				,     _lowercase=5_1_2				,     _lowercase=2				,     _lowercase=0.02				,     _lowercase=1E-12				,     _lowercase=1				,     _lowercase=0				,     _lowercase=2				,     _lowercase="absolute"				,     _lowercase=True				,     _lowercase=None				,     **_lowercase				,     )			->    str:







								'''simple docstring'''

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

								snake_case_ :			List[Any]            =      vocab_size
								snake_case_ :			str            =      hidden_size
								snake_case_ :			Any            =      num_hidden_layers
								snake_case_ :			Optional[Any]            =      num_attention_heads
								snake_case_ :			Tuple            =      hidden_act
								snake_case_ :			str            =      intermediate_size
								snake_case_ :			Dict            =      hidden_dropout_prob
								snake_case_ :			Optional[Any]            =      attention_probs_dropout_prob
								snake_case_ :			Optional[int]            =      max_position_embeddings
								snake_case_ :			str            =      type_vocab_size
								snake_case_ :			Tuple            =      initializer_range
								snake_case_ :			Optional[Any]            =      layer_norm_eps
								snake_case_ :			Optional[int]            =      position_embedding_type
								snake_case_ :			List[Any]            =      use_cache
								snake_case_ :			Tuple            =      classifier_dropout




class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		@property
		def  UpperCAmelCase__				(       self )			->    Mapping[str, Mapping[int, str]]:







								'''simple docstring'''

								if self.task == "multiple-choice":
														snake_case_ :			Union[str, Any]            =      {0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
														snake_case_ :			Optional[Any]            =      {0: """batch""", 1: """sequence"""}
								return OrderedDict(
								    [
								        ("""input_ids""", dynamic_axis),
								        ("""attention_mask""", dynamic_axis),
								    ] )








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        __magic_name__	(				lowerCamelCase__     ,					lowerCamelCase__     ,					unittest.TestCase				):





     """simple docstring"""



     __UpperCamelCase            =  IFInpaintingPipeline
     __UpperCamelCase            =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
     __UpperCamelCase            =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     __UpperCamelCase            =  PipelineTesterMixin.required_optional_params - {'''latents'''}



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[Any]	):





        '''simple docstring'''




        return self._get_dummy_components()



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]      ,   snake_case							:str      ,   snake_case							:List[Any]=0	):





        '''simple docstring'''




        if str(snake_case	).startswith("mps"	):
           A_       :					Optional[Any]        =      torch.manual_seed(snake_case	)
        else:
           A_       :					Any        =      torch.Generator(device=snake_case	).manual_seed(snake_case	)

        A_       :					Optional[Any]        =      floats_tensor((1, 3, 32, 32)      ,   rng=random.Random(snake_case	)	).to(snake_case	)
        A_       :					Any        =      floats_tensor((1, 3, 32, 32)      ,   rng=random.Random(snake_case	)	).to(snake_case	)

        A_       :					List[Any]        =      {
            "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     SCREAMING_SNAKE_CASE		(				self							:Any	):





        '''simple docstring'''




        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)



     def     SCREAMING_SNAKE_CASE		(				self							:Optional[int]	):





        '''simple docstring'''




        self._test_save_load_optional_components()



     @unittest.skipIf(torch_device != "cuda"      ,   reason="float16 requires CUDA"	)
     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]	):





        '''simple docstring'''




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



     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)



     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        self._test_save_load_local()





     def     SCREAMING_SNAKE_CASE		(				self							:Dict	):





        '''simple docstring'''




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