
'''simple docstring'''






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 			UpperCAmelCase__    (							A    ,						A    ,						unittest.TestCase			):
    lowerCAmelCase_					     =		IFInpaintingSuperResolutionPipeline
    lowerCAmelCase_					     =		TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
    lowerCAmelCase_					     =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}			)
    lowerCAmelCase_					     =		PipelineTesterMixin.required_optional_params - {'latents'}



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          return self._get_superresolution_dummy_components()



    def 	lowerCamelCase_	(						self   :							str,__A   :							List[str],__A   :							List[str]=0  ):
          if str(__A  ).startswith("mps"  ):
                _lowerCamelCase					: List[str]		    = torch.manual_seed(__A  )
          else:
                _lowerCamelCase					: Optional[int]		    = torch.Generator(device=__A  ).manual_seed(__A  )

          _lowerCamelCase					: List[Any]		    = floats_tensor((1, 3, 1_6, 1_6),rng=random.Random(__A  )  ).to(__A  )
          _lowerCamelCase					: Any		    = floats_tensor((1, 3, 3_2, 3_2),rng=random.Random(__A  )  ).to(__A  )
          _lowerCamelCase					: Tuple		    = floats_tensor((1, 3, 3_2, 3_2),rng=random.Random(__A  )  ).to(__A  )

          _lowerCamelCase					: Dict		    = {
              "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 	lowerCamelCase_	(						self   :							Optional[int]  ):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )



    def 	lowerCamelCase_	(						self   :							Dict  ):
          self._test_save_load_optional_components()



    @unittest.skipIf(torch_device != "cuda",reason="float16 requires CUDA"  )
    def 	lowerCamelCase_	(						self   :							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  )



    def 	lowerCamelCase_	(						self   :							Any  ):
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2  )



    def 	lowerCamelCase_	(						self   :							Dict  ):
          self._test_save_load_local()



    def 	lowerCamelCase_	(						self   :							Any  ):
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2,)

from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase				=			logging.get_logger(__name__)

__lowerCamelCase				=			{
    """kssteven/ibert-roberta-base""": """https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json""",
    """kssteven/ibert-roberta-large""": """https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json""",
    """kssteven/ibert-roberta-large-mnli""": (
        """https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"""
    ),
}






class     UpperCAmelCase						(		A_					):
   A__   :						Optional[int]			=	"ibert"



   def __init__(self						:   List[Any]       ,		snake_case__						:   int=3_05_22       ,		snake_case__						:   int=7_68       ,		snake_case__						:   Any=12       ,		snake_case__						:   str=12       ,		snake_case__						:   Optional[int]=30_72       ,		snake_case__						:   Union[str, Any]="gelu"       ,		snake_case__						:   Any=0.1       ,		snake_case__						:   List[Any]=0.1       ,		snake_case__						:   str=5_12       ,		snake_case__						:   Optional[int]=2       ,		snake_case__						:   Any=0.02       ,		snake_case__						:   List[Any]=1e-12       ,		snake_case__						:   Optional[Any]=1       ,		snake_case__						:   List[Any]=0       ,		snake_case__						:   str=2       ,		snake_case__						:   Tuple="absolute"       ,		snake_case__						:   List[str]=False       ,		snake_case__						:   Tuple="none"       ,		**snake_case__						:   Tuple       ,		)						->      Union[str, Any]:


          '''simple docstring'''


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

          snake_case			:  str     							= vocab_size
          snake_case			:  Tuple     							= hidden_size
          snake_case			:  Optional[Any]     							= num_hidden_layers
          snake_case			:  Optional[int]     							= num_attention_heads
          snake_case			:  List[Any]     							= hidden_act
          snake_case			:  Optional[Any]     							= intermediate_size
          snake_case			:  Dict     							= hidden_dropout_prob
          snake_case			:  List[str]     							= attention_probs_dropout_prob
          snake_case			:  List[str]     							= max_position_embeddings
          snake_case			:  Union[str, Any]     							= type_vocab_size
          snake_case			:  Optional[int]     							= initializer_range
          snake_case			:  Optional[int]     							= layer_norm_eps
          snake_case			:  int     							= position_embedding_type
          snake_case			:  Union[str, Any]     							= quant_mode
          snake_case			:  Dict     							= force_dequant






class     UpperCAmelCase						(		A_					):



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


          '''simple docstring'''


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





