


import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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   __lowercase				(  __snake_case ,					__snake_case ,					unittest.TestCase  ):
      UpperCamelCase								=       IFInpaintingPipeline
      UpperCamelCase								=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
      UpperCamelCase								=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCamelCase								=       PipelineTesterMixin.required_optional_params - {'''latents'''}





      def        _lowercase				(		self						:					Any       )    ->   int:






         """simple docstring"""







         return self._get_dummy_components()





      def        _lowercase				(		self						:					Optional[Any]					,       __lowerCamelCase						:					Optional[int]					,       __lowerCamelCase						:					str=0       )    ->   Dict:






         """simple docstring"""







         if str(__lowerCamelCase       ).startswith("""mps"""       ):
            UpperCAmelCase							=     torch.manual_seed(__lowerCamelCase       )
         else:
            UpperCAmelCase							=     torch.Generator(device=__lowerCamelCase       ).manual_seed(__lowerCamelCase       )

         UpperCAmelCase							=     floats_tensor((1, 3, 3_2, 3_2)					,       rng=random.Random(__lowerCamelCase       )       ).to(__lowerCamelCase       )
         UpperCAmelCase							=     floats_tensor((1, 3, 3_2, 3_2)					,       rng=random.Random(__lowerCamelCase       )       ).to(__lowerCamelCase       )

         UpperCAmelCase							=     {
             """prompt""": """A painting of a squirrel eating a burger""",
             """image""": 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        _lowercase				(		self						:					Dict       )    ->   List[Any]:






         """simple docstring"""







         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )





      def        _lowercase				(		self						:					Union[str, Any]       )    ->   Optional[int]:






         """simple docstring"""







         self._test_save_load_optional_components()





      @unittest.skipIf(torch_device != """cuda"""					,       reason="""float16 requires CUDA"""       )
      def        _lowercase				(		self						:					int       )    ->   List[str]:






         """simple docstring"""







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





      def        _lowercase				(		self						:					Dict       )    ->   List[str]:






         """simple docstring"""







         self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   Any:






         """simple docstring"""







         self._test_save_load_local()





      def        _lowercase				(		self						:					Tuple       )    ->   str:






         """simple docstring"""







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







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


__a     =			logging.get_logger(__name__)

__a     =			{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       '''ctrl'''
      UpperCamelCase								=       ['''past_key_values''']
      UpperCamelCase								=       {
          '''max_position_embeddings''': '''n_positions''',
          '''hidden_size''': '''n_embd''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }





      def __init__(		self						:					str					,       __lowerCamelCase						:					Optional[int]=2_4_6_5_3_4					,       __lowerCamelCase						:					Union[str, Any]=2_5_6					,       __lowerCamelCase						:					int=1_2_8_0					,       __lowerCamelCase						:					Optional[Any]=8_1_9_2					,       __lowerCamelCase						:					List[str]=4_8					,       __lowerCamelCase						:					Dict=1_6					,       __lowerCamelCase						:					Union[str, Any]=0.1					,       __lowerCamelCase						:					Union[str, Any]=0.1					,       __lowerCamelCase						:					Union[str, Any]=1e-6					,       __lowerCamelCase						:					str=0.02					,       __lowerCamelCase						:					Union[str, Any]=True					,       **__lowerCamelCase						:					List[str]					,       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     vocab_size
         UpperCAmelCase							=     n_positions
         UpperCAmelCase							=     n_embd
         UpperCAmelCase							=     n_layer
         UpperCAmelCase							=     n_head
         UpperCAmelCase							=     dff
         UpperCAmelCase							=     resid_pdrop
         UpperCAmelCase							=     embd_pdrop
         UpperCAmelCase							=     layer_norm_epsilon
         UpperCAmelCase							=     initializer_range

         UpperCAmelCase							=     use_cache

         super().__init__(**__lowerCamelCase       )



