

import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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 SCREAMING_SNAKE_CASE	(  a_   ,							a_   ,							unittest.TestCase ):
  """simple docstring"""






  lowerCamelCase    :			Tuple						      =IFImgaImgPipeline
  lowerCamelCase    :			Tuple						      =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
  lowerCamelCase    :			int						      =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
  lowerCamelCase    :			Any						      =PipelineTesterMixin.required_optional_params - {"latents"}







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[int]    )							->	Dict:






     """simple docstring"""







     return self._get_dummy_components()







  def        SCREAMING_SNAKE_CASE    (			self :    Any       ,							lowerCAmelCase :    str       ,							lowerCAmelCase :    Union[str, Any]=0    )							->	int:






     """simple docstring"""







     if str(lowerCAmelCase    ).startswith("""mps"""    ):
        __lowerCAmelCase  :			int					    =						torch.manual_seed(lowerCAmelCase    )
     else:
        __lowerCAmelCase  :			Union[str, Any]					    =						torch.Generator(device=lowerCAmelCase    ).manual_seed(lowerCAmelCase    )

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

     __lowerCAmelCase  :			Optional[int]					    =						{
         """prompt""": """A painting of a squirrel eating a burger""",
         """image""": image,
         """generator""": generator,
         """num_inference_steps""": 2,
         """output_type""": """numpy""",
     }

     return inputs







  def        SCREAMING_SNAKE_CASE    (			self :    Dict    )							->	int:






     """simple docstring"""







     self._test_save_load_optional_components()







  @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    )							->	Any:






     """simple docstring"""







     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )







  @unittest.skipIf(torch_device != """cuda"""       ,							reason="""float16 requires CUDA"""    )
  def        SCREAMING_SNAKE_CASE    (			self :    Any    )							->	Dict:






     """simple docstring"""







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







  @unittest.skipIf(torch_device != """cuda"""       ,							reason="""float16 requires CUDA"""    )
  def        SCREAMING_SNAKE_CASE    (			self :    Tuple    )							->	str:






     """simple docstring"""







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







  def        SCREAMING_SNAKE_CASE    (			self :    str    )							->	List[str]:






     """simple docstring"""







     self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[int]    )							->	Dict:






     """simple docstring"""







     self._test_save_load_local()







  def        SCREAMING_SNAKE_CASE    (			self :    Optional[int]    )							->	int:






     """simple docstring"""







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





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


__UpperCAmelCase      = logging.get_logger(__name__)

__UpperCAmelCase      = {
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}



class SCREAMING_SNAKE_CASE	(  a_ ):
  """simple docstring"""






  lowerCamelCase    :			Optional[Any]						      ="nystromformer"







  def __init__(			self :    List[str]       ,							lowerCAmelCase :    List[Any]=3_00_00       ,							lowerCAmelCase :    Union[str, Any]=7_68       ,							lowerCAmelCase :    int=12       ,							lowerCAmelCase :    str=12       ,							lowerCAmelCase :    Tuple=30_72       ,							lowerCAmelCase :    Optional[Any]="gelu_new"       ,							lowerCAmelCase :    Any=0.1       ,							lowerCAmelCase :    Tuple=0.1       ,							lowerCAmelCase :    Tuple=5_10       ,							lowerCAmelCase :    int=2       ,							lowerCAmelCase :    Any=64       ,							lowerCAmelCase :    Dict=64       ,							lowerCAmelCase :    List[str]=65       ,							lowerCAmelCase :    Union[str, Any]=False       ,							lowerCAmelCase :    int=0.02       ,							lowerCAmelCase :    int=1e-5       ,							lowerCAmelCase :    Dict=1       ,							lowerCAmelCase :    str=0       ,							lowerCAmelCase :    List[Any]=2       ,							**lowerCAmelCase :    int       ,							)							->	List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :			Optional[Any]					    =						vocab_size
     __lowerCAmelCase  :			Any					    =						max_position_embeddings
     __lowerCAmelCase  :			List[Any]					    =						hidden_size
     __lowerCAmelCase  :			Dict					    =						num_hidden_layers
     __lowerCAmelCase  :			Union[str, Any]					    =						num_attention_heads
     __lowerCAmelCase  :			int					    =						intermediate_size
     __lowerCAmelCase  :			str					    =						hidden_act
     __lowerCAmelCase  :			Any					    =						hidden_dropout_prob
     __lowerCAmelCase  :			str					    =						attention_probs_dropout_prob
     __lowerCAmelCase  :			List[str]					    =						initializer_range
     __lowerCAmelCase  :			List[str]					    =						type_vocab_size
     __lowerCAmelCase  :			Union[str, Any]					    =						segment_means_seq_len
     __lowerCAmelCase  :			Tuple					    =						num_landmarks
     __lowerCAmelCase  :			Dict					    =						conv_kernel_size
     __lowerCAmelCase  :			Tuple					    =						inv_coeff_init_option
     __lowerCAmelCase  :			List[Any]					    =						layer_norm_eps
     super().__init__(pad_token_id=lowerCAmelCase       ,							bos_token_id=lowerCAmelCase       ,							eos_token_id=lowerCAmelCase       ,							**lowerCAmelCase    )


