






"""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  __lowerCamelCase     (	_a				,						_a				,						unittest.TestCase	):
   a   :					List[Any]			    =IFSuperResolutionPipeline
   a   :					int			    =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
   a   :					int			    =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   a   :					List[Any]			    =PipelineTesterMixin.required_optional_params - {"""latents"""}

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       return self._get_superresolution_dummy_components()

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_=0   )		->							Any:
       if str(snake_case_   ).startswith('mps'   ):
           UpperCamelCase__								=     torch.manual_seed(snake_case_   )
       else:
           UpperCamelCase__								=     torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )

       UpperCamelCase__								=     floats_tensor((1, 3, 32, 32)			,					rng=random.Random(snake_case_   )   ).to(snake_case_   )

       UpperCamelCase__								=     {
           '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     SCREAMING_SNAKE_CASE__			(       self   )		->							int:
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							int:
       self._test_save_load_optional_components()

   @unittest.skipIf(torch_device != 'cuda'			,					reason='float16 requires CUDA'   )
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Union[str, 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     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[Any]:
       self._test_save_load_local()







   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[Any]:
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2			,					)












"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


A__       :			Union[str, Any]=						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  __lowerCamelCase     (	_a	):
   a   :					Optional[int]			    ="""rembert"""

   def __init__(       self			,					snake_case_=25_0300			,					snake_case_=1152			,					snake_case_=32			,					snake_case_=18			,					snake_case_=256			,					snake_case_=1664			,					snake_case_=4608			,					snake_case_="gelu"			,					snake_case_=0.0			,					snake_case_=0.0			,					snake_case_=0.1			,					snake_case_=512			,					snake_case_=2			,					snake_case_=0.02			,					snake_case_=1E-12			,					snake_case_=True			,					snake_case_=0			,					snake_case_=312			,					snake_case_=313			,					**snake_case_			,					)		->							str:
       super().__init__(pad_token_id=snake_case_			,					bos_token_id=snake_case_			,					eos_token_id=snake_case_			,					**snake_case_   )

       UpperCamelCase__								=     vocab_size
       UpperCamelCase__								=     input_embedding_size
       UpperCamelCase__								=     output_embedding_size
       UpperCamelCase__								=     max_position_embeddings
       UpperCamelCase__								=     hidden_size
       UpperCamelCase__								=     num_hidden_layers
       UpperCamelCase__								=     num_attention_heads
       UpperCamelCase__								=     intermediate_size
       UpperCamelCase__								=     hidden_act
       UpperCamelCase__								=     hidden_dropout_prob
       UpperCamelCase__								=     attention_probs_dropout_prob
       UpperCamelCase__								=     classifier_dropout_prob
       UpperCamelCase__								=     initializer_range
       UpperCamelCase__								=     type_vocab_size
       UpperCamelCase__								=     layer_norm_eps
       UpperCamelCase__								=     use_cache
       UpperCamelCase__								=     False







class  __lowerCamelCase     (	_a	):

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
           UpperCamelCase__								=     {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
           UpperCamelCase__								=     {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('token_type_ids', dynamic_axis),
           ]   )







   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							float:
       return 1E-4




