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


a__				:      List[Any]    		=     logging.get_logger(__name__)

a__				:      List[Any]    		=     {
    """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"""
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}






class     a_       (			_snake_case							):



       """simple docstring"""
       __SCREAMING_SNAKE_CASE       :					List[str]						=  """cpmant"""






       def __init__(		self				,       _lowerCamelCase = 3_0720				,       _lowerCamelCase = 4096				,       _lowerCamelCase = 32				,       _lowerCamelCase = 128				,       _lowerCamelCase = 1_0240				,       _lowerCamelCase = 48				,       _lowerCamelCase = 0.0				,       _lowerCamelCase = 512				,       _lowerCamelCase = 2048				,       _lowerCamelCase = 1e-6				,       _lowerCamelCase = 1.0				,       _lowerCamelCase = 32				,       _lowerCamelCase = 32				,       _lowerCamelCase = 32				,       _lowerCamelCase = True				,       **_lowerCamelCase				,       )					->Tuple:
           super().__init__(**snake_case_   )
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = prompt_types
           SCREAMING_SNAKE_CASE				:				Optional[Any]				      = prompt_length
           SCREAMING_SNAKE_CASE				:				List[str]				      = segment_types
           SCREAMING_SNAKE_CASE				:				int				      = hidden_size
           SCREAMING_SNAKE_CASE				:				str				      = num_attention_heads
           SCREAMING_SNAKE_CASE				:				List[str]				      = dim_head
           SCREAMING_SNAKE_CASE				:				List[str]				      = dim_ff
           SCREAMING_SNAKE_CASE				:				str				      = num_hidden_layers
           SCREAMING_SNAKE_CASE				:				int				      = position_bias_num_buckets
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = position_bias_max_distance
           SCREAMING_SNAKE_CASE				:				Union[str, Any]				      = dropout_p
           SCREAMING_SNAKE_CASE				:				str				      = eps
           SCREAMING_SNAKE_CASE				:				Dict				      = use_cache
           SCREAMING_SNAKE_CASE				:				str				      = vocab_size
           SCREAMING_SNAKE_CASE				:				Optional[int]				      = init_std


"""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    ,     )

