
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


_a   :      str       =				logging.get_logger(__name__)

_a   :      Dict       =				{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			int	=       """rembert"""
    def __init__(     self    ,     snake_case_=25_03_00    ,     snake_case_=11_52    ,     snake_case_=32    ,     snake_case_=18    ,     snake_case_=2_56    ,     snake_case_=16_64    ,     snake_case_=46_08    ,     snake_case_="gelu"    ,     snake_case_=0.0    ,     snake_case_=0.0    ,     snake_case_=0.1    ,     snake_case_=5_12    ,     snake_case_=2    ,     snake_case_=0.02    ,     snake_case_=1E-12    ,     snake_case_=True    ,     snake_case_=0    ,     snake_case_=3_12    ,     snake_case_=3_13    ,     **snake_case_    ,     ):
      super().__init__(pad_token_id=snake_case_    ,     bos_token_id=snake_case_    ,     eos_token_id=snake_case_    ,     **snake_case_  )

      _snake_case  :    Tuple            = vocab_size
      _snake_case  :    Optional[int]            = input_embedding_size
      _snake_case  :    Optional[Any]            = output_embedding_size
      _snake_case  :    Dict            = max_position_embeddings
      _snake_case  :    str            = hidden_size
      _snake_case  :    Any            = num_hidden_layers
      _snake_case  :    int            = num_attention_heads
      _snake_case  :    str            = intermediate_size
      _snake_case  :    Dict            = hidden_act
      _snake_case  :    Dict            = hidden_dropout_prob
      _snake_case  :    Tuple            = attention_probs_dropout_prob
      _snake_case  :    Optional[int]            = classifier_dropout_prob
      _snake_case  :    Optional[int]            = initializer_range
      _snake_case  :    int            = type_vocab_size
      _snake_case  :    Optional[int]            = layer_norm_eps
      _snake_case  :    List[Any]            = use_cache
      _snake_case  :    Optional[Any]            = False






class   _UpperCAmelCase					(       _snake_case):
    @property
    def        lowerCamelCase__						(     self  ):
      if self.task == "multiple-choice":
        _snake_case  :    Optional[int]            = {0: "batch", 1: "choice", 2: "sequence"}
      else:
        _snake_case  :    Any            = {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("token_type_ids", dynamic_axis),
          ]  )


    @property
    def        lowerCamelCase__						(     self  ):
      return 1E-4



"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
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   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			List[str]	=       IFSuperResolutionPipeline
    __lowercase				:			Optional[Any]	=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
    __lowercase				:			List[Any]	=       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    __lowercase				:			Optional[Any]	=       PipelineTesterMixin.required_optional_params - {"""latents"""}
    def        lowerCamelCase__						(     self  ):
      return self._get_superresolution_dummy_components()
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=0  ):
      if str(snake_case_  ).startswith("mps"  ):
        _snake_case  :    Tuple            = torch.manual_seed(snake_case_  )
      else:
        _snake_case  :    Tuple            = torch.Generator(device=snake_case_  ).manual_seed(snake_case_  )

      _snake_case  :    List[str]            = floats_tensor((1, 3, 32, 32)    ,     rng=random.Random(snake_case_  )  ).to(snake_case_  )

      _snake_case  :    List[str]            = {
          "prompt": "A painting of a squirrel eating a burger",
          "image": 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  ):
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_optional_components()
    @unittest.skipIf(torch_device != "cuda"    ,     reason="float16 requires CUDA"  )
    def        lowerCamelCase__						(     self  ):
      # 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  ):
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2  )
    def        lowerCamelCase__						(     self  ):
      self._test_save_load_local()


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

